{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "helper load finish!!!\n"
     ]
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "from base_helper import *\n",
    "test_op = get_operation_round1_new()\n",
    "train_op = get_operation_train_new()\n",
    "train_tst = get_transaction_train_new()\n",
    "test_op = get_transaction_round1_new()\n",
    "\n",
    "tag = get_tag_train_new()\n",
    "\n",
    "train_ops = pd.merge(train_op, tag, on='UID', how='left')\n",
    "train_tsts =  pd.merge(train_tst, tag, on='UID', how='left')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['submit_example.csv',\n",
       " 'test_operation_round2.csv',\n",
       " 'test_transaction_round2.csv']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import os\n",
    "os.listdir('./data/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "test_trans = pd.read_csv('./data/test_transaction_round2.csv')\n",
    "test_op = pd.read_csv('./data/test_operation_round2.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sn\n",
    "import matplotlib as plt\n",
    "def plot_s(data, col):\n",
    "    temp = data.groupby([col])['Tag'].agg({\n",
    "    \"tag_cnt\": lambda x: x.count(),\n",
    "    \"tag_sum\": np.sum\n",
    "    }).reset_index()\n",
    "    temp[\"tag_sum_cnt\"] = temp['tag_sum'] / temp['tag_cnt']\n",
    "    temp = temp.sort_values('tag_sum_cnt')\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# operation table columns and the tag analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UID             0.000000\n",
       "channel         0.000000\n",
       "day             0.000000\n",
       "time            0.000000\n",
       "trans_amt       0.000000\n",
       "amt_src1        0.000000\n",
       "merchant        0.000000\n",
       "code1           0.924240\n",
       "code2           0.985904\n",
       "trans_type1     0.000000\n",
       "acc_id1         0.626023\n",
       "device_code1    0.367390\n",
       "device_code2    0.396974\n",
       "device_code3    0.817565\n",
       "device1         0.187956\n",
       "device2         0.172262\n",
       "mac1            0.367568\n",
       "ip1             0.189412\n",
       "bal             0.000000\n",
       "amt_src2        0.491375\n",
       "acc_id2         0.887964\n",
       "acc_id3         0.885727\n",
       "geo_code        0.269208\n",
       "trans_type2     0.219628\n",
       "market_code     0.932566\n",
       "market_type     0.932566\n",
       "ip1_sub         0.189412\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - test_op.count(axis=0) / test_op.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UID             0.000000\n",
       "day             0.000000\n",
       "mode            0.000000\n",
       "success         0.069193\n",
       "time            0.000000\n",
       "os              0.000000\n",
       "version         0.172581\n",
       "device1         0.172110\n",
       "device2         0.293160\n",
       "device_code1    0.282713\n",
       "device_code2    0.304609\n",
       "device_code3    0.889977\n",
       "mac1            0.899273\n",
       "mac2            0.385850\n",
       "ip1             0.179653\n",
       "ip2             0.907332\n",
       "wifi            0.687139\n",
       "geo_code        0.339111\n",
       "ip1_sub         0.179653\n",
       "ip2_sub         0.907332\n",
       "Tag             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - train_ops.count(axis=0) / train_ops.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'trans_amt'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3062\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3063\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3064\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'trans_amt'",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-8-0aa80a209646>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[0mcols\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;34m'trans_amt'\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_ops\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munique\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      3\u001b[0m \u001b[0mtrain_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgeo_code\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtrain_ops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgeo_code\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[0mdf1\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot_s\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtrain_ops\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcols\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[0msn\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mjointplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mcols\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0my\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tag_sum_cnt'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2683\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2684\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2685\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2686\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2687\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_getitem_column\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_column\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2690\u001b[0m         \u001b[1;31m# get column\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2691\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_unique\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2692\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_item_cache\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2693\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2694\u001b[0m         \u001b[1;31m# duplicate columns & possible reduce dimensionality\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36m_get_item_cache\u001b[1;34m(self, item)\u001b[0m\n\u001b[0;32m   2484\u001b[0m         \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcache\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2485\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2486\u001b[1;33m             \u001b[0mvalues\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_data\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2487\u001b[0m             \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_box_item_values\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2488\u001b[0m             \u001b[0mcache\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mget\u001b[1;34m(self, item, fastpath)\u001b[0m\n\u001b[0;32m   4113\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4114\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4115\u001b[1;33m                 \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4116\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4117\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mD:\\anaconda\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3063\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3064\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3065\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3066\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3067\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'trans_amt'"
     ]
    }
   ],
   "source": [
    "cols = 'trans_amt'\n",
    "print(train_ops[cols].unique())\n",
    "train_ops.geo_code = train_ops.geo_code.str[:2]\n",
    "df1 = plot_s(train_ops, cols)\n",
    "sn.jointplot(data=df1, x=cols, y='tag_sum_cnt')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>f3bdbd82e0d55978</td>\n",
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       "      <td>0.000000</td>\n",
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       "    <tr>\n",
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       "      <td>290ead084a839c02</td>\n",
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       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4d24371c232305b3</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>443ba9b183118540</td>\n",
       "      <td>624</td>\n",
       "      <td>8</td>\n",
       "      <td>0.012821</td>\n",
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       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>e1b96d504829cadf</td>\n",
       "      <td>117</td>\n",
       "      <td>6</td>\n",
       "      <td>0.051282</td>\n",
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       "    <tr>\n",
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       "      <td>4c3d45ed38eefb7a</td>\n",
       "      <td>2782</td>\n",
       "      <td>143</td>\n",
       "      <td>0.051402</td>\n",
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       "      <td>20a91b45ef8f8221</td>\n",
       "      <td>20231</td>\n",
       "      <td>1044</td>\n",
       "      <td>0.051604</td>\n",
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       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>c8741ce15ceac2a4</td>\n",
       "      <td>800059</td>\n",
       "      <td>56283</td>\n",
       "      <td>0.070349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>4d475374a72edc03</td>\n",
       "      <td>351</td>\n",
       "      <td>26</td>\n",
       "      <td>0.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>479617cd802ed152</td>\n",
       "      <td>7346</td>\n",
       "      <td>644</td>\n",
       "      <td>0.087667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>bf79b3647c0878eb</td>\n",
       "      <td>9993</td>\n",
       "      <td>1121</td>\n",
       "      <td>0.112179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>072eee5c88d380df</td>\n",
       "      <td>27494</td>\n",
       "      <td>3114</td>\n",
       "      <td>0.113261</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>b501fa4fc58206b9</td>\n",
       "      <td>59132</td>\n",
       "      <td>6753</td>\n",
       "      <td>0.114202</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1c341176507fbd9b</td>\n",
       "      <td>11892</td>\n",
       "      <td>1536</td>\n",
       "      <td>0.129162</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>8cbc4686cfb509dd</td>\n",
       "      <td>217</td>\n",
       "      <td>29</td>\n",
       "      <td>0.133641</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>9c69742a831d6214</td>\n",
       "      <td>2799</td>\n",
       "      <td>395</td>\n",
       "      <td>0.141122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>ac63e881c4e19402</td>\n",
       "      <td>3769</td>\n",
       "      <td>541</td>\n",
       "      <td>0.143539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>d25caee90b27fa9b</td>\n",
       "      <td>184574</td>\n",
       "      <td>26764</td>\n",
       "      <td>0.145004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08017d2cb28c2348</td>\n",
       "      <td>5100</td>\n",
       "      <td>742</td>\n",
       "      <td>0.145490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>963bd8a75ff9ab37</td>\n",
       "      <td>22169</td>\n",
       "      <td>3584</td>\n",
       "      <td>0.161667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>8c7ce5ff939e66d1</td>\n",
       "      <td>2703</td>\n",
       "      <td>446</td>\n",
       "      <td>0.165002</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2f3e878175e34d9c</td>\n",
       "      <td>11824</td>\n",
       "      <td>2162</td>\n",
       "      <td>0.182848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2e6fdb7d4ddef57f</td>\n",
       "      <td>72</td>\n",
       "      <td>14</td>\n",
       "      <td>0.194444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>8e463287d7146285</td>\n",
       "      <td>25</td>\n",
       "      <td>5</td>\n",
       "      <td>0.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>270ca3ca8f0d1126</td>\n",
       "      <td>2219</td>\n",
       "      <td>507</td>\n",
       "      <td>0.228481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>79f8b86938180c3c</td>\n",
       "      <td>82435</td>\n",
       "      <td>23994</td>\n",
       "      <td>0.291066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>acfaded7e04e7ba0</td>\n",
       "      <td>51041</td>\n",
       "      <td>18997</td>\n",
       "      <td>0.372191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>a698c1862f390021</td>\n",
       "      <td>1174</td>\n",
       "      <td>473</td>\n",
       "      <td>0.402896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>a3f0d631c4fcaf38</td>\n",
       "      <td>37</td>\n",
       "      <td>19</td>\n",
       "      <td>0.513514</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>9faf791e291fe4a1</td>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "      <td>0.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>385608c64026c669</td>\n",
       "      <td>240</td>\n",
       "      <td>218</td>\n",
       "      <td>0.908333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2e7e5808ff6323e8</td>\n",
       "      <td>85</td>\n",
       "      <td>84</td>\n",
       "      <td>0.988235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>8b259df485199a2b</td>\n",
       "      <td>71</td>\n",
       "      <td>71</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>e00efcf34eb17f69</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>89 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                mode  tag_cnt  tag_sum  tag_sum_cnt\n",
       "60  ae18d98d880f65bc        4        0     0.000000\n",
       "72  d22abe3bc961e5f2        6        0     0.000000\n",
       "25  44aa2d1440730479        1        0     0.000000\n",
       "70  ce2ef1dc219082d9        1        0     0.000000\n",
       "35  6dd34621d82476a0       11        0     0.000000\n",
       "75  d48cfe8d9921f5b6       22        0     0.000000\n",
       "52  a4aef803edb7874d       61        0     0.000000\n",
       "50  a18c79737c17f4f9        8        0     0.000000\n",
       "41  8bda7a6747b7ad7a        2        0     0.000000\n",
       "18  30293957fcf5681c       20        0     0.000000\n",
       "76  da11300b15a56456       18        0     0.000000\n",
       "64  be215a1560dcbf6e       13        0     0.000000\n",
       "79  e0fc956bf4f3dfb7        2        0     0.000000\n",
       "54  a6a93673bd79224c        4        0     0.000000\n",
       "14  2ab549beba0a4482       12        0     0.000000\n",
       "87  f4b0872cd482e33f       22        0     0.000000\n",
       "46  926aacd05a540d5d        9        0     0.000000\n",
       "82  e352f410afb1878a        1        0     0.000000\n",
       "9   1f287477e8f4b95a        1        0     0.000000\n",
       "8   1cbe3d31395984d7        1        0     0.000000\n",
       "56  ab37f730c615850f       38        0     0.000000\n",
       "6   148dc2618a3a92a4        1        0     0.000000\n",
       "5   12845a3fe90eb1de       27        0     0.000000\n",
       "4   0e72a7851fadf00b        6        0     0.000000\n",
       "84  e88d8e8ad8a93013       52        0     0.000000\n",
       "85  f1e83bce32405f92       56        0     0.000000\n",
       "86  f3bdbd82e0d55978        8        0     0.000000\n",
       "13  290ead084a839c02       47        0     0.000000\n",
       "30  4d24371c232305b3        1        0     0.000000\n",
       "24  443ba9b183118540      624        8     0.012821\n",
       "..               ...      ...      ...          ...\n",
       "80  e1b96d504829cadf      117        6     0.051282\n",
       "29  4c3d45ed38eefb7a     2782      143     0.051402\n",
       "10  20a91b45ef8f8221    20231     1044     0.051604\n",
       "66  c8741ce15ceac2a4   800059    56283     0.070349\n",
       "31  4d475374a72edc03      351       26     0.074074\n",
       "26  479617cd802ed152     7346      644     0.087667\n",
       "65  bf79b3647c0878eb     9993     1121     0.112179\n",
       "1   072eee5c88d380df    27494     3114     0.113261\n",
       "62  b501fa4fc58206b9    59132     6753     0.114202\n",
       "7   1c341176507fbd9b    11892     1536     0.129162\n",
       "43  8cbc4686cfb509dd      217       29     0.133641\n",
       "48  9c69742a831d6214     2799      395     0.141122\n",
       "57  ac63e881c4e19402     3769      541     0.143539\n",
       "73  d25caee90b27fa9b   184574    26764     0.145004\n",
       "2   08017d2cb28c2348     5100      742     0.145490\n",
       "47  963bd8a75ff9ab37    22169     3584     0.161667\n",
       "42  8c7ce5ff939e66d1     2703      446     0.165002\n",
       "17  2f3e878175e34d9c    11824     2162     0.182848\n",
       "15  2e6fdb7d4ddef57f       72       14     0.194444\n",
       "44  8e463287d7146285       25        5     0.200000\n",
       "11  270ca3ca8f0d1126     2219      507     0.228481\n",
       "37  79f8b86938180c3c    82435    23994     0.291066\n",
       "58  acfaded7e04e7ba0    51041    18997     0.372191\n",
       "53  a698c1862f390021     1174      473     0.402896\n",
       "51  a3f0d631c4fcaf38       37       19     0.513514\n",
       "49  9faf791e291fe4a1        9        6     0.666667\n",
       "22  385608c64026c669      240      218     0.908333\n",
       "16  2e7e5808ff6323e8       85       84     0.988235\n",
       "40  8b259df485199a2b       71       71     1.000000\n",
       "78  e00efcf34eb17f69        5        5     1.000000\n",
       "\n",
       "[89 rows x 4 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "mode_col = ['e00efcf34eb17f69', '8b259df485199a2b', '2e7e5808ff6323e8']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>geo_code</th>\n",
       "      <th>tag_cnt</th>\n",
       "      <th>tag_sum</th>\n",
       "      <th>tag_sum_cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9m</td>\n",
       "      <td>25</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>wp</td>\n",
       "      <td>1270</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>w5</td>\n",
       "      <td>72</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>w4</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>w1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>vp</td>\n",
       "      <td>19</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>vn</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>ur</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>wc</td>\n",
       "      <td>11</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>tu</td>\n",
       "      <td>112</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ty</td>\n",
       "      <td>108</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>tx</td>\n",
       "      <td>68</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>tw</td>\n",
       "      <td>31</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>u0</td>\n",
       "      <td>8</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>wj</td>\n",
       "      <td>1157</td>\n",
       "      <td>5</td>\n",
       "      <td>0.004322</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>wn</td>\n",
       "      <td>1410</td>\n",
       "      <td>22</td>\n",
       "      <td>0.015603</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>tz</td>\n",
       "      <td>5051</td>\n",
       "      <td>85</td>\n",
       "      <td>0.016828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>wz</td>\n",
       "      <td>4075</td>\n",
       "      <td>90</td>\n",
       "      <td>0.022086</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>y9</td>\n",
       "      <td>2203</td>\n",
       "      <td>49</td>\n",
       "      <td>0.022242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>wx</td>\n",
       "      <td>116535</td>\n",
       "      <td>2664</td>\n",
       "      <td>0.022860</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>wh</td>\n",
       "      <td>10231</td>\n",
       "      <td>267</td>\n",
       "      <td>0.026097</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>yb</td>\n",
       "      <td>13215</td>\n",
       "      <td>399</td>\n",
       "      <td>0.030193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>wr</td>\n",
       "      <td>15036</td>\n",
       "      <td>526</td>\n",
       "      <td>0.034983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>vb</td>\n",
       "      <td>4635</td>\n",
       "      <td>236</td>\n",
       "      <td>0.050917</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>uz</td>\n",
       "      <td>268</td>\n",
       "      <td>14</td>\n",
       "      <td>0.052239</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>wt</td>\n",
       "      <td>217122</td>\n",
       "      <td>11384</td>\n",
       "      <td>0.052431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>y8</td>\n",
       "      <td>27438</td>\n",
       "      <td>1522</td>\n",
       "      <td>0.055471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>wm</td>\n",
       "      <td>159329</td>\n",
       "      <td>10293</td>\n",
       "      <td>0.064602</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>wq</td>\n",
       "      <td>69143</td>\n",
       "      <td>4601</td>\n",
       "      <td>0.066543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>tv</td>\n",
       "      <td>380</td>\n",
       "      <td>26</td>\n",
       "      <td>0.068421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>ww</td>\n",
       "      <td>88678</td>\n",
       "      <td>6161</td>\n",
       "      <td>0.069476</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>wk</td>\n",
       "      <td>78123</td>\n",
       "      <td>7891</td>\n",
       "      <td>0.101007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>w7</td>\n",
       "      <td>6516</td>\n",
       "      <td>772</td>\n",
       "      <td>0.118478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>ws</td>\n",
       "      <td>139101</td>\n",
       "      <td>17393</td>\n",
       "      <td>0.125039</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>ux</td>\n",
       "      <td>895</td>\n",
       "      <td>193</td>\n",
       "      <td>0.215642</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>we</td>\n",
       "      <td>2083</td>\n",
       "      <td>485</td>\n",
       "      <td>0.232837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>wu</td>\n",
       "      <td>36</td>\n",
       "      <td>34</td>\n",
       "      <td>0.944444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>wd</td>\n",
       "      <td>1057</td>\n",
       "      <td>1055</td>\n",
       "      <td>0.998108</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   geo_code  tag_cnt  tag_sum  tag_sum_cnt\n",
       "0        9m       25        0     0.000000\n",
       "26       wp     1270        0     0.000000\n",
       "16       w5       72        0     0.000000\n",
       "15       w4        1        0     0.000000\n",
       "14       w1        2        0     0.000000\n",
       "13       vp       19        0     0.000000\n",
       "12       vn        6        0     0.000000\n",
       "8        ur        5        0     0.000000\n",
       "18       wc       11        0     0.000000\n",
       "1        tu      112        0     0.000000\n",
       "5        ty      108        0     0.000000\n",
       "4        tx       68        0     0.000000\n",
       "3        tw       31        0     0.000000\n",
       "7        u0        8        0     0.000000\n",
       "22       wj     1157        5     0.004322\n",
       "25       wn     1410       22     0.015603\n",
       "6        tz     5051       85     0.016828\n",
       "34       wz     4075       90     0.022086\n",
       "36       y9     2203       49     0.022242\n",
       "33       wx   116535     2664     0.022860\n",
       "21       wh    10231      267     0.026097\n",
       "37       yb    13215      399     0.030193\n",
       "28       wr    15036      526     0.034983\n",
       "11       vb     4635      236     0.050917\n",
       "10       uz      268       14     0.052239\n",
       "30       wt   217122    11384     0.052431\n",
       "35       y8    27438     1522     0.055471\n",
       "24       wm   159329    10293     0.064602\n",
       "27       wq    69143     4601     0.066543\n",
       "2        tv      380       26     0.068421\n",
       "32       ww    88678     6161     0.069476\n",
       "23       wk    78123     7891     0.101007\n",
       "17       w7     6516      772     0.118478\n",
       "29       ws   139101    17393     0.125039\n",
       "9        ux      895      193     0.215642\n",
       "20       we     2083      485     0.232837\n",
       "31       wu       36       34     0.944444\n",
       "19       wd     1057     1055     0.998108"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'00094ae2a1d62504',\n",
       " '12845a3fe90eb1de',\n",
       " '148dc2618a3a92a4',\n",
       " '20a91b45ef8f8221',\n",
       " '272b66487a69658a',\n",
       " '2e7e5808ff6323e8',\n",
       " '33518cc0c2151469',\n",
       " '3b02bcd4b685d7a8',\n",
       " '443ba9b183118540',\n",
       " '44aa2d1440730479',\n",
       " '479617cd802ed152',\n",
       " '4b55abf363c83025',\n",
       " '4d24371c232305b3',\n",
       " '6b2aa6745680f08a',\n",
       " '6dd34621d82476a0',\n",
       " '745961ec187394cd',\n",
       " '79f8b86938180c3c',\n",
       " '8b259df485199a2b',\n",
       " '8e463287d7146285',\n",
       " '9037ea11b74dccb9',\n",
       " '9faf791e291fe4a1',\n",
       " 'a3f0d631c4fcaf38',\n",
       " 'a4aef803edb7874d',\n",
       " 'ab37f730c615850f',\n",
       " 'ad7b22964af6d026',\n",
       " 'be215a1560dcbf6e',\n",
       " 'cc273b0b2a2afb0f',\n",
       " 'cca82161b59127c1',\n",
       " 'cd75f033b0488ace',\n",
       " 'ce2ef1dc219082d9',\n",
       " 'd21cae2b73884a8d',\n",
       " 'dd06c9d2ea0a3750',\n",
       " 'e00efcf34eb17f69',\n",
       " 'e0fc956bf4f3dfb7',\n",
       " 'e1d2fad8ffdc66b3',\n",
       " 'e352f410afb1878a'}"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(df1[cols]) - set(test_op[cols])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mode</th>\n",
       "      <th>UID</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>028c7b2172a63a44</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>57</th>\n",
       "      <td>d22abe3bc961e5f2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>51</th>\n",
       "      <td>b33675e39153768c</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>85481fffc420edca</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1cbe3d31395984d7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>ae18d98d880f65bc</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>a6a93673bd79224c</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>8bda7a6747b7ad7a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>66</th>\n",
       "      <td>ead94ed7824d5bf0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0e72a7851fadf00b</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1f287477e8f4b95a</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>a18c79737c17f4f9</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>43bd7c1135abe71a</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>55</th>\n",
       "      <td>c5bdf60179ae58d7</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>926aacd05a540d5d</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>f3bdbd82e0d55978</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>844de77cbda0a8ee</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>f1e83bce32405f92</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>da9f055c25d788dc</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2ab549beba0a4482</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>f9cb1243ae2c0b2a</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>da11300b15a56456</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>30293957fcf5681c</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4949910f9a417183</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2e6fdb7d4ddef57f</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>f4b0872cd482e33f</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>290ead084a839c02</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60</th>\n",
       "      <td>d48cfe8d9921f5b6</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>42a54af5b2d73c77</td>\n",
       "      <td>59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>9105f3241c9ca194</td>\n",
       "      <td>61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>8a3ad6c9e44c1659</td>\n",
       "      <td>983</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>a698c1862f390021</td>\n",
       "      <td>1155</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>270ca3ca8f0d1126</td>\n",
       "      <td>1494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>3f6258f4825c2251</td>\n",
       "      <td>1512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>a96a05783c922913</td>\n",
       "      <td>1653</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53</th>\n",
       "      <td>b668e42707ee9c7b</td>\n",
       "      <td>1756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>b0d4cd119fbef366</td>\n",
       "      <td>2051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>17a6ee1a7b6a02b7</td>\n",
       "      <td>2360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>8c7ce5ff939e66d1</td>\n",
       "      <td>2705</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>9c69742a831d6214</td>\n",
       "      <td>3112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>6440f87037199039</td>\n",
       "      <td>3256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>fc147276b8ff76fb</td>\n",
       "      <td>3318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4c3d45ed38eefb7a</td>\n",
       "      <td>3400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>ac63e881c4e19402</td>\n",
       "      <td>4015</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>08017d2cb28c2348</td>\n",
       "      <td>6099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>37031ef728f5c886</td>\n",
       "      <td>6619</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>09080b31b40d57e8</td>\n",
       "      <td>7266</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>e806d126013b42d1</td>\n",
       "      <td>8018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>963bd8a75ff9ab37</td>\n",
       "      <td>8152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2f3e878175e34d9c</td>\n",
       "      <td>10558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1c341176507fbd9b</td>\n",
       "      <td>10775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>6216c95d48247735</td>\n",
       "      <td>11041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30fb6b175a9ca918</td>\n",
       "      <td>15341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>680d16190f2d0390</td>\n",
       "      <td>15901</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>54</th>\n",
       "      <td>bf79b3647c0878eb</td>\n",
       "      <td>16191</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>52</th>\n",
       "      <td>b501fa4fc58206b9</td>\n",
       "      <td>24527</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>072eee5c88d380df</td>\n",
       "      <td>45949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>acfaded7e04e7ba0</td>\n",
       "      <td>48415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>d25caee90b27fa9b</td>\n",
       "      <td>99902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>56</th>\n",
       "      <td>c8741ce15ceac2a4</td>\n",
       "      <td>768702</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                mode     UID\n",
       "0   028c7b2172a63a44       1\n",
       "57  d22abe3bc961e5f2       1\n",
       "51  b33675e39153768c       1\n",
       "33  85481fffc420edca       1\n",
       "7   1cbe3d31395984d7       1\n",
       "49  ae18d98d880f65bc       2\n",
       "44  a6a93673bd79224c       2\n",
       "35  8bda7a6747b7ad7a       2\n",
       "66  ead94ed7824d5bf0       3\n",
       "4   0e72a7851fadf00b       3\n",
       "8   1f287477e8f4b95a       5\n",
       "42  a18c79737c17f4f9       5\n",
       "22  43bd7c1135abe71a       5\n",
       "55  c5bdf60179ae58d7       6\n",
       "39  926aacd05a540d5d       8\n",
       "68  f3bdbd82e0d55978      14\n",
       "31  844de77cbda0a8ee      15\n",
       "67  f1e83bce32405f92      16\n",
       "62  da9f055c25d788dc      17\n",
       "12  2ab549beba0a4482      18\n",
       "70  f9cb1243ae2c0b2a      22\n",
       "61  da11300b15a56456      28\n",
       "15  30293957fcf5681c      28\n",
       "23  4949910f9a417183      35\n",
       "13  2e6fdb7d4ddef57f      44\n",
       "69  f4b0872cd482e33f      47\n",
       "11  290ead084a839c02      47\n",
       "60  d48cfe8d9921f5b6      54\n",
       "21  42a54af5b2d73c77      59\n",
       "38  9105f3241c9ca194      61\n",
       "..               ...     ...\n",
       "34  8a3ad6c9e44c1659     983\n",
       "43  a698c1862f390021    1155\n",
       "10  270ca3ca8f0d1126    1494\n",
       "20  3f6258f4825c2251    1512\n",
       "45  a96a05783c922913    1653\n",
       "53  b668e42707ee9c7b    1756\n",
       "50  b0d4cd119fbef366    2051\n",
       "5   17a6ee1a7b6a02b7    2360\n",
       "36  8c7ce5ff939e66d1    2705\n",
       "41  9c69742a831d6214    3112\n",
       "29  6440f87037199039    3256\n",
       "71  fc147276b8ff76fb    3318\n",
       "25  4c3d45ed38eefb7a    3400\n",
       "47  ac63e881c4e19402    4015\n",
       "2   08017d2cb28c2348    6099\n",
       "18  37031ef728f5c886    6619\n",
       "3   09080b31b40d57e8    7266\n",
       "64  e806d126013b42d1    8018\n",
       "40  963bd8a75ff9ab37    8152\n",
       "14  2f3e878175e34d9c   10558\n",
       "6   1c341176507fbd9b   10775\n",
       "28  6216c95d48247735   11041\n",
       "16  30fb6b175a9ca918   15341\n",
       "30  680d16190f2d0390   15901\n",
       "54  bf79b3647c0878eb   16191\n",
       "52  b501fa4fc58206b9   24527\n",
       "1   072eee5c88d380df   45949\n",
       "48  acfaded7e04e7ba0   48415\n",
       "58  d25caee90b27fa9b   99902\n",
       "56  c8741ce15ceac2a4  768702\n",
       "\n",
       "[72 rows x 2 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_op.geo_code = test_op.geo_code.str[:2]\n",
    "test_op1 = test_op.groupby(cols, as_index=False).count()[[cols, 'UID']].sort_values('UID')\n",
    "\n",
    "test_op1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "58 39\n"
     ]
    }
   ],
   "source": [
    "test_geo = set(test_op.geo_code.str[:2])\n",
    "train_geo = set(train_ops.geo_code.str[:2])\n",
    "print(len(test_geo), len(train_geo))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{nan,\n",
       " 'tu',\n",
       " 'tv',\n",
       " 'tw',\n",
       " 'tx',\n",
       " 'ty',\n",
       " 'tz',\n",
       " 'u0',\n",
       " 'uz',\n",
       " 'vb',\n",
       " 'w1',\n",
       " 'w5',\n",
       " 'w7',\n",
       " 'wc',\n",
       " 'wd',\n",
       " 'we',\n",
       " 'wh',\n",
       " 'wj',\n",
       " 'wk',\n",
       " 'wm',\n",
       " 'wn',\n",
       " 'wp',\n",
       " 'wq',\n",
       " 'wr',\n",
       " 'ws',\n",
       " 'wt',\n",
       " 'wu',\n",
       " 'ww',\n",
       " 'wx',\n",
       " 'wz',\n",
       " 'y8',\n",
       " 'y9',\n",
       " 'yb'}"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_geo & train_geo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'9m',\n",
       " nan,\n",
       " 'tu',\n",
       " 'tv',\n",
       " 'tw',\n",
       " 'tx',\n",
       " 'ty',\n",
       " 'tz',\n",
       " 'u0',\n",
       " 'ur',\n",
       " 'ux',\n",
       " 'uz',\n",
       " 'vb',\n",
       " 'vn',\n",
       " 'vp',\n",
       " 'w1',\n",
       " 'w4',\n",
       " 'w5',\n",
       " 'w7',\n",
       " 'wc',\n",
       " 'wd',\n",
       " 'we',\n",
       " 'wh',\n",
       " 'wj',\n",
       " 'wk',\n",
       " 'wm',\n",
       " 'wn',\n",
       " 'wp',\n",
       " 'wq',\n",
       " 'wr',\n",
       " 'ws',\n",
       " 'wt',\n",
       " 'wu',\n",
       " 'ww',\n",
       " 'wx',\n",
       " 'wz',\n",
       " 'y8',\n",
       " 'y9',\n",
       " 'yb'}"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "set(train_ops.geo_code.str[:2]) & "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# transction table columns and the tag analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['UID', 'channel', 'day', 'time', 'trans_amt', 'amt_src1', 'merchant',\n",
       "       'code1', 'code2', 'trans_type1', 'acc_id1', 'device_code1',\n",
       "       'device_code2', 'device_code3', 'device1', 'device2', 'mac1', 'ip1',\n",
       "       'bal', 'amt_src2', 'acc_id2', 'acc_id3', 'geo_code', 'trans_type2',\n",
       "       'market_code', 'market_type', 'ip1_sub', 'Tag'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_tsts.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UID             0.000000\n",
       "channel         0.000000\n",
       "day             0.000000\n",
       "time            0.000000\n",
       "trans_amt       0.000000\n",
       "amt_src1        0.000000\n",
       "merchant        0.030596\n",
       "code1           0.997484\n",
       "code2           0.999977\n",
       "trans_type1     0.000000\n",
       "acc_id1         0.527231\n",
       "device_code1    0.228724\n",
       "device_code2    0.251780\n",
       "device_code3    0.771276\n",
       "device1         0.007361\n",
       "device2         0.007548\n",
       "mac1            0.230554\n",
       "ip1             0.000000\n",
       "bal             0.000000\n",
       "amt_src2        0.390561\n",
       "acc_id2         0.819531\n",
       "acc_id3         0.838981\n",
       "geo_code        0.094873\n",
       "trans_type2     0.373074\n",
       "market_code     0.917566\n",
       "market_type     0.917566\n",
       "ip1_sub         0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - test_trans.count(axis=0) / test_trans.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "UID             0.000000\n",
       "channel         0.000000\n",
       "day             0.000000\n",
       "time            0.000000\n",
       "trans_amt       0.000000\n",
       "amt_src1        0.000000\n",
       "merchant        0.000000\n",
       "code1           0.906009\n",
       "code2           0.987837\n",
       "trans_type1     0.000000\n",
       "acc_id1         0.620947\n",
       "device_code1    0.342013\n",
       "device_code2    0.368625\n",
       "device_code3    0.835158\n",
       "device1         0.179729\n",
       "device2         0.147532\n",
       "mac1            0.344639\n",
       "ip1             0.146334\n",
       "bal             0.000000\n",
       "amt_src2        0.463681\n",
       "acc_id2         0.891360\n",
       "acc_id3         0.890465\n",
       "geo_code        0.254899\n",
       "trans_type2     0.132830\n",
       "market_code     0.886550\n",
       "market_type     0.886550\n",
       "ip1_sub         0.146334\n",
       "Tag             0.000000\n",
       "dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - train_tsts.count(axis=0) / train_tsts.shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "D:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x4e214208>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ERBJSQImISJAUUCIiEiQFlIiIBEkBJSIiQcqLXnxVq+qYW13D5voGBhYXUVkxnOnj9xqTVkREAhL5gKpaVccNi9bQ0NgEQF19AzcsWgOgkBIRCVjkT/HNra5pCadmDY1NzK2uyVJFIiKSisgH1Ob6hi4tFxGRMEQ+oAYWF3VpuYiIhCHyAVVZMZyiwoI2y4oKC6isGJ7kFSIiEoLId5Jo7gihXnwiIrkl8gEFsZBSIImI5JbIn+ITEZHcFPkjqKpVddy0eC31DY0AHLJ/ITd+6TgdUYmIBC7SAVW1qo7KR16mcc8nUy7v2NlI5aMvA7pRV0QkZJE+xTe3uqZNODVrbHLdqCsiErhIB1RHN+PqRl0RkbBFOqA6uhm3eP/CHqxERES6KtIBddqIAUnX+d5n/kREJCCRDqhn39iWdN178V59IiISpkgHVEfXmTQWn4hI2CIdUMlCyEBj8YmIBC7SAZVooFgDZk4conugREQCF+kbdTVQrIhI7op0QIEGihURyVWRPsUnIiK5SwElIiJBUkCJiEiQIn8NqmpVnTpJiIjkoEgHVNWqOm5YtIaGxiYA6uobuGHRGkBTbYiIhC7Sp/jmVte0hFOzhsYmTbUhIpIDIh1QdUmGOkq2XEREwhHpgCow69JyEREJR6QDqinJnBrJlouISDgiHVAdHSlVrarrwUpERKSrIh1QHR0p3fy7tT1YiYiIdFWkA6qjI6gdOzVhoYhIyCIdULrWJCKSuyIdUB0dQRUXFfZgJSIi0lWRDqiOjqBumnZcD1YiIiJdFemAGpRkyvfiokINdSQiErhIB1SiKd+LCgt09CQikgMiPVispnwXEcldGQ0oM5sK/AQoAH7p7re1Wz8EWAAUx9vMdvcl6axBU76LiOSmjAWUmRUAdwFTgFrgJTNb7O6vtWo2B3jY3X9mZqOAJcDQdNYx856lvLh+e5tlF04cwg+nj0nn24iISJpl8hrUBGCdu29w913Ar4Fz2rVx4KD444OBzeksIFE4Afz3sreZU7UmnW8lIiJplsmAGgRsavW8Nr6stZuAC82sltjR09XpLCBRODV7aPmmpOtERCT7MhlQie6SbX9j0gxgvruXAGcCvzKzvWoys8vNbIWZrdi2bVtaitMoEyISqkx85uWiTAZULTC41fMS9j6FdwnwMIC7LwX6Aoe135C7z3P3cncvHzBgQFqK05xQIhKqTHzm5aJMBtRLQKmZHWVmfYALgMXt2rwNnA5gZiOJBVTa/lyYdEz/pOsmHn1Iut5GREQyIGMB5e67gauAauB1Yr311prZLWY2Ld7sOuAyM3sZeAiY5Z6+c28PXHYihUn2cOO7mvZdRCRkGb0PKn5P05J2y77f6vFrwKRM1rB7T+Llm+sVUCIiIYv0UEcAA5OMx5dsuYiIhCHyAXXaiAF7dScsKiygsmJ4VuoREZHURDqgqlbV8ZuVdW36thvw5c9q+CMRkdBFOqDmVtfQ0NjUZpkDz76Rv/cViIjkikgHVLKOEOogISISvkgHVPH+mtZdRCRXRTqg6nc2JlzuwAk/erpnixERkS6JdEB1dMfvPz7Y1WN1iIhI10U6oEREJHcpoEREJEiRDqjSww9Iuu6Ifn16sBIREemqSAfU09dOThhSR/Trw/LvTslCRSIikqpIBxTAt04r5ZBW3c2Liwq54cxRWaxIRERSkdHRzLOtalUdlY++TGPTJ/356hsaqXzkZQANdyQiErBIH0HNra5pE07NGvc4c6trslCRiIikKtIB1dGQRhruSEQkbJEOqI6GOtJ8UCIiYYt0QHU0ebzmgxIRCVukA+q9hsRj8YE6SIiIhC7SAZXsNN4hGuVcRCR4kQ6oyorhFPRqP+E7fPjxbqpW1WWhIhERSVWkAwqgaY+6mYuI5KJIB1RHIaRu5iIiYYt0QNV1EELqZi4iErZIB1SB7X39qZm6mYuIhC3SAdXUwY1Q6mYuIhK2SAfUoCSn8ZItFxGRcEQ6oCorhlNUWNBmWVFhgU7viYjkgEhPt9F8Gm9udQ2b6xsYWFxEZcVwnd4TEckBkT6CAvjOwtXU1TfgxHr1fWfhao6a/US2yxIRkU5EOqCGJgkiB4WUiEjgIh1QHelgoHMREQlA3gaUiIiETQElIiJBytuASj7GhIiIhCDSAbXxtrMSLjfgrSTrREQkDJG+DwqSh5SIiIQt8gFVtaqOmxavpT4+/fsh+xdy45eO0826IiKBi3RAVa2q49qHV9N6zsIdOxupfPRlQAPGioiELNLXoG7+3VoSTKhLY5Nm1BURCV2kA2rHzsak6zSjrohI2CIdUB3RjLoiImHL24DSlBsiImHL24BSBwkRkbBFOqB6JRkuItlyEREJR6QDar/eiXcv2XIREQlHpD+pP27c06XlIiISjowGlJlNNbMaM1tnZrOTtPmqmb1mZmvN7MF0vn+ynnr79ylI59uIiEgGdBpQZnZUKssStCkA7gLOAEYBM8xsVLs2pcANwCR3Pw74Top1p6SyYjgFCS44fbSriTlVa9L5ViIikmapHEH9JsGyR1N43QRgnbtvcPddwK+Bc9q1uQy4y913ALj71hS2m7Lp4wexJ9FQEsBDyzel861ERCTNko7FZ2YjgOOAg83svFarDgL6prDtQUDrFKgFTmjX5tj4e70IFAA3ufuTCWq5HLgcYMiQISm89SeSTe3e5Jr0XUTCtC+feVHS0WCxw4GzgWLgS62Wf0DsyKcziTpzt0+F3kApMBkoAf5sZqPdvb7Ni9znAfMAysvLU06WqlV1qTYVEQlGdz/zoiZpQLn7Y8BjZnaiuy/txrZrgcGtnpcAmxO0WebujcBbZlZDLLBe6sb77UUDwoqI5K5UpttYZ2b/Gxjaur27f7OT170ElMY7VNQBFwBfb9emCpgBzDezw4id8tuQWumd62hA2EEai09EJGipBNRjwJ+BPwBNqW7Y3Xeb2VVANbHrS/e5+1ozuwVY4e6L4+u+aGavxbdd6e7vdnUnkhlYXERdgpAyNBafiEjoUgmo/d39+u5s3N2XAEvaLft+q8cOXBv/SrvKiuHcsGgNDY2f5KoBMycO0Vh8IiKBS6Wb+eNmdmbGK8mA6eMH8eXPDqLAYv01CsyYOXEIP5w+JsuViYhIZ1IJqG8TC6kGM3vfzD4ws/czXVg6VK2q4zcr61q6lDe585uVderdJyKSAzoNKHfv5+693L3I3Q+KPz+oJ4rbV3Ora9qc3gNoaGxS7z4RkRyQylBH55rZwa2eF5vZ9MyWlR7JevFpuncRkfClcorvRnd/r/lJ/CbaGzNXUvokGyxW072LiIQvlYBK1CaV3n9ZV1kxnKLCtiOXFxUWqIu5iEgOSCVoVpjZj4mNTO7A1cDKjFaVJs1dyedW17C5voGBxUVUVgxXF3MRkRyQSkBdDXwPWBh//hQwJ2MVpdn08YMUSCIiOajTgHL3j4CEkw0CmNn/cfer01qViIjkvXTMqDspDdsQERFpIyc6O+yLqlV1ugYlIpKDIh1QVavq2ozFV1ffwA2LYlO9K6RERMKWjlN8iSYmDEKykSRu/t3aLFUkIiKpSkdA/SQN28iIRFNtAOzY2ajx+EREApfKUEflZvZbM/ubmb1iZmvM7JXm9e4+P6MV7oPmUcwT0VGUiEjYUrkG9QBQCawB9mS2nPRqHsU8kR07G3uwEhER6apUAmpbfPbbnDMoyYy6IiK5YvtHu7JdQtakElA3mtkvgWeAfzYvdPdFGasqTU4bMYD/XvZ2wnXFRYU9XI2IiHRFKgH1DWAEUMgnp/gcCD6gnn1jW9J1N007rgcrERGRrkoloMrcPSfnSO9o3qfvLFzN3Ooa3bgrIhKoVLqZLzOzURmvJAOSd5GIab5xV13ORUTCk0pAnQysNrOaRN3Mc52mgBcRCVMqp/imZryKLNMU8CISqv4H9Ml2CVmTSkB1dqYs52kKeBGR8KQSUE8QCykD+gJHATVAJLrBaQp4EZEwdXoNyt3HuPvY+L+lwATghcyXtu8unDikw/WDiou49bwx6sUnIhKgLk+34e5/M7PjM1FMunV0H9Sg4iJenP35HqxGRES6otOAMrNrWz3tBXwWSP7JH5COOj+oY4SISNhS6Wber9XXfsDjwDmZLCpdOur8oI4RIiJh6/QIyt1vbn5sZr2AA93944xWlSaVFcO59uHV7GnXD7GwwNQxQkQkcKnMB/WgmR1kZgcArwE1ZlaZ+dLSI9GcUF87frA6RoiIBC6VU3yj3P19YDqwBBgC/EtGq0qTudU1NLY/fKLjzhMiIhKGVAKq0MwKiQXUY+7eSI7cvJtsLijNESUiEr5UAuoXwEbgAOB5M/s08H4mi0qXZFO+dzQVvIiIhCGVG3V/6u6D3P1Md3fgbeC05vVmdnEmC9wXyaZ872gqeBERCUMqR1BteMzuVou+ncZ60uqAPgVdWi4iIuHockAlEOz5so92NXVpuYiIhCMdAaXzZSIiknaRPoISEZHclY6AejEN28iIosLEu6dEFREJX1cHi232HrDS3Ve7+1XpLys9Sg4p4s2tH+213IGZ9yzlgctO7PmiREQkJakcQZUDVwCD4l+XA5OBe8zs3zNX2r5LFE7NXly/vQcrERGRrkplPqhDgc+4+4cAZnYj8ChwCrASuCNz5YmISL5K5QhqCLCr1fNG4NPu3gD8MyNViYhI3kvlCOpBYJmZPRZ//iXgoVajm+ekScf0z3YJIiLSgVTmg/qBmf0emESsA9wV7r4ivnpmJovLJHWQEBEJW0rdzOOB9BCwCNhqZkNSeZ2ZTTWzGjNbZ2azO2h3vpm5mZWnVLWIiEReKhMWTjOzN4G3gD/F//19Cq8rAO4CzgBGATPMbFSCdv2Aa4DlXStdRESiLJUjqB8AE4G/u/tRwBdI7ebcCcA6d9/g7ruAXwPnJNn+HUCPTiM/p2pNT76diIh0USoB1eju7wK9zKyXuz8LjEvhdYOATa2e18aXtTCz8cBgd3881YLT5aHlmzpvJCIiWZNKL756MzsQeB54wMy2Eutq3plEIwq1DCxrZr2AO4FZnW7I7HJiNwgzZEhKl786pTmhRCRUmfjMy0WpHEG9DOwE/g14ElgPvJHC62qBwa2elwCbWz3vB4wGnjOzjcROIy5O1FHC3ee5e7m7lw8YMCCFt+6cZtUVkVBl4jMvF6VyBHWau+8B9gALAMzslRRe9xJQamZHAXXABcDXm1e6+3vAYc3Pzew54H+16sKeUTNOGNx5IxERyZqkAWVmVwL/ChzTLpD6kUInCXffbWZXAdVAAXCfu681s1uAFe6+eN9K3zc/nD4mm28vIiKd6OgI6kFi3clvBVrfw/SBu6c00qq7LwGWtFv2/SRtJ6eyza7Yv7AXOxv37LW8QGf3RESCl/QalLu/5+4b3X2Gu/9Pq6+cGQY8UTgBNLm6mYuIhC4dExbmJHUzFxEJW94GlLqZi4iELW8DqpeuQ4mIBC1vA2q/3nm76yIiOSFvP6U/TtKBQkREwpC3ATWwuCjbJYiISAfyNqAqK4ZnuwQREelA3gbU9PGDOm8kIiJZk7cBJSIiYYt0QBUVRnr3REQiLdKf4A0d9NQbOvuJHqxERKR7tn+0K9slZE2kA0pERHKXAkpERIKkgBIRkSApoEREJEh5G1CDNJKEiEjQ8jagThsxINsliIh0qv8BfbJdQtbkbUA9+8a2bJcgIiIdyNuA2lzfkO0SRESkA3kbUMX7F2a7BBER6UDeBpRmfBcRCVveBtR7DY3ZLkFERDrQO9sFZEvzhIVVq+qYW13D5voGBhYXUVkxXFNxiIgEIG8DqrJiOFWr6rhh0RoaGpsAqKtv4IZFawDNFyUikm15e4pv+vhBzK2uaQmnZg2NTcytrslSVSIi0ixvAwqSdzVXF3QRkezL64AamGS4o2TLRUSk5+RtQFWtqqOyYjhFhQVtlhcVFlBZMTxLVYmISLO8Dai51TVMHz+IW88bw6DiIozYALK3njdGHSRERAKQt734mq8zTR8/SIEkIhKgvD2C0nUmEZGw5W1AaboNEZGw5W1APfHKlmyXICLSqe0f7cp2CVmTtwG1Y2cjVavqsl2GiIgkkbcBBWjECBGRgOV1QGnECBGRcOV1QKknn4jZgqHTAAANVElEQVRIuPI6oDRihIhIuPI2oIqLCnWDrogEr/8BfbJdQtbkbUBpRl0RkbDlbUDp+pOISNjyNqCGHqqAEhEJWd4G1NIN27NdgoiIdCBvA2qPZ7sCERHpSN4GlIhILtBYfCIiIoHJaECZ2VQzqzGzdWY2O8H6a83sNTN7xcyeMbNPZ7Ke1oqLCnvqrUREpBsyFlBmVgDcBZwBjAJmmNmods1WAeXuPhZ4FLgjU/W09/Gu3T31ViIi0g2ZPIKaAKxz9w3uvgv4NXBO6wbu/qy774w/XQaUZLCeNj5uUi8JEZGQZTKgBgGbWj2vjS9L5hLg94lWmNnlZrbCzFZs27YtjSWKiISn9Wde0873sl1O1mQyoCzBsoSHLWZ2IVAOzE203t3nuXu5u5cPGKCp2kUk2vSZF5PJgKoFBrd6XgJsbt/IzL4AfBeY5u7/zGA9e9GMuiIi4cpkQL0ElJrZUWbWB7gAWNy6gZmNB35BLJy2ZrCWhG5YtEYhJSISqIwFlLvvBq4CqoHXgYfdfa2Z3WJm0+LN5gIHAo+Y2WozW5xkcxnR0Nikad9FRALVO5Mbd/clwJJ2y77f6vEXMvn+qdC07yIiYcr7kSQ07YaIhExDHeWposICTfsuIhKovA6oW88bo2nfRUQCldcBpXASEQlXXgeUiIiEK28DKtEwFyIiEo68DahepogSEQlZ3gZUk2s0cxGRkOVtQAFMuu2PGupIRCRQeR1QdfUNGo9PRCRQeR1QoPH4RERClfcBBRqPT0QkRAooNB6fiEiI8j6gNB6fiEiYMjrdRugGFRdRWTFcQx6JiAQorwPqxdmfz3YJIiKSRN6f4hMRkTApoEREJEh5G1D7F+btrouI5IS8/ZTu07sg2yWIiEgH8jag6hsas12CiIh0IG8DSkREwqaAEhGRICmgREQkSAooEREJkgJKRESCpIASEZEgKaBERCRICigREQmSAkpERIKU1wFVtaou2yWIiEgSeR1QNyxao5ASEQlUXgdUQ2MTc6trsl2GiIgkkNcBBbC5viHbJYiISAJ5H1ADi4uyXYKIiCSQ1wFVVFhAZcXwbJchIiIJ5HVAffmzg5g+flC2yxARkQTyOqCefWNbtksQEZEk8jqg1EFCRCRceR1Q6iAhIhKuvA6oj/65m6NmP8Gk2/6oG3ZFRALTO9sFZFN9QyMAdfUN3LBoDYA6TYiIBCKvj6Ba06gSIiJhUUC1ok4TIiLhyOtTfO2p04SIhOjB5W9nu4S0+PoJQ7rUXkdQcRpVQkQkLBkNKDObamY1ZrbOzGYnWL+fmS2Mr19uZkMzWU97h+xfiAGDiou49bwx6iAhIhKQjJ3iM7MC4C5gClALvGRmi939tVbNLgF2uPswM7sAuB34WqZqam/Hzk968X1n4Wq+s3B1j7xvgRkzThjMD6eP6ZH3a69qVR1zq2vYXN/AwOIiKiuGdxrO3XmNSJTpdyLzMnkENQFY5+4b3H0X8GvgnHZtzgEWxB8/CpxuZpbBmoLQ5M5/L3ubOVVrevy9q1bVccOiNdTVN+B80sW+o/vAuvMakSjT70TPyGRADQI2tXpeG1+WsI277wbeAw7NYE1BeWj5ps4bpdnc6hoaGpvaLOusi313XiMSZfqd6BmZ7MWX6EjIu9EGM7scuBxgyJCu9QIJWZPvtasZl6wrfUdd7LvzGpEoy/TvRPvPvK72fouKTB5B1QKDWz0vATYna2NmvYGDge3tN+Tu89y93N3LBwwYkKFye15BFs5mJutK31EX++68RiTKMv07EdXPvK7KZEC9BJSa2VFm1ge4AFjcrs1i4OL44/OBP7pn4bAiS2acMLjzRmlWWTGcosKCNss662LfndeIRJl+J3pGxgIqfk3pKqAaeB142N3XmtktZjYt3uxe4FAzWwdcC+zVFX1fbLztrHRuLm0KzLhw4pCs9OKbPn4Qt543hkHFRSl3se/Oa0SiTL8TPcNy7YClvLzcV6xYke0yRET2VUrn+CP6mZfSvmskCRERCZICSkREgqSAEhGRICmgREQkSAooEREJkgJKRESCpIASEZEgKaBERCRICigREQmSAkpERIKkgBIRkSDl3Fh8ZrYN+J9uvPQw4J00l5MtUdkX7UdYtB896x13n9pZIzN7MpV2UZRzAdVdZrbC3cuzXUc6RGVftB9h0X5IaHSKT0REgqSAEhGRIOVTQM3LdgFpFJV90X6ERfshQcmba1AiIpJb8ukISkREcogCSkREghS5gDKzqWZWY2brzGx2gvX7mdnC+PrlZja056vsXAr7ca2ZvWZmr5jZM2b26WzU2ZnO9qNVu/PNzM0syO7BqeyHmX01/j1Za2YP9nSNqUrhZ2uImT1rZqviP19nZqPOjpjZfWa21cxeTbLezOyn8X18xcw+09M1Shq4e2S+gAJgPXA00Ad4GRjVrs2/Aj+PP74AWJjturu5H6cB+8cfX5mr+xFv1w94HlgGlGe77m5+P0qBVcAh8eeHZ7vufdiXecCV8cejgI3ZrjvBfpwCfAZ4Ncn6M4HfAwZMBJZnu2Z9df0rakdQE4B17r7B3XcBvwbOadfmHGBB/PGjwOlmZj1YYyo63Q93f9bdd8afLgNKerjGVKTy/QD4AXAH8HFPFtcFqezHZcBd7r4DwN239nCNqUplXxw4KP74YGBzD9aXEnd/HtjeQZNzgPs9ZhlQbGZH9kx1ki5RC6hBwKZWz2vjyxK2cffdwHvAoT1SXepS2Y/WLiH212JoOt0PMxsPDHb3x3uysC5K5ftxLHCsmb1oZsvMLNShaVLZl5uAC82sFlgCXN0zpaVVV3+HJEC9s11AmiU6Emrfjz6VNtmWco1mdiFQDpya0Yq6p8P9MLNewJ3ArJ4qqJtS+X70JnaabzKxo9k/m9lod6/PcG1dlcq+zADmu/t/mNmJwK/i+7In8+WlTS78nksnonYEVQsMbvW8hL1PT7S0MbPexE5hdHSqIBtS2Q/M7AvAd4Fp7v7PHqqtKzrbj37AaOA5M9tI7FrB4gA7SqT6c/WYuze6+1tADbHACk0q+3IJ8DCAuy8F+hIbgDWXpPQ7JGGLWkC9BJSa2VFm1odYJ4jF7dosBi6OPz4f+KO7h/aXVaf7ET819gti4RTq9Y4O98Pd33P3w9x9qLsPJXYtbZq7r8hOuUml8nNVRazjCmZ2GLFTfht6tMrUpLIvbwOnA5jZSGIBta1Hq9x3i4GL4r35JgLvufuWbBclXROpU3zuvtvMrgKqifVWus/d15rZLcAKd18M3EvslMU6YkdOF2Sv4sRS3I+5wIHAI/E+Hm+7+7SsFZ1AivsRvBT3oxr4opm9BjQBle7+bvaqTizFfbkOuMfM/o3YabFZof0RZ2YPETudelj8WtmNQCGAu/+c2LWzM4F1wE7gG9mpVPaFhjoSEZEgRe0Un4iIRIQCSkREgqSAEhGRICmgREQkSAooEZEu6Gyg2nZtgx94N2QKKMk5ZlZsZv+a7Tq6ysymm9mobNch+2w+kOpQVnOAh919PLFbWu7OVFFRpICSXFRMbFT6NsysIAu1dMV0YqODSw5LNFCtmR1jZk+a2Uoz+7OZjWhuTuAD74ZM90FJzjGz5hG4a4BG4ENgCzDO3UeZWRWxYW76Aj9x93nx130I/AQ4G2gAznH3f5jZV4jd6NlEbMSBU5K871DgV8AB8UVXuftfzGwycDPwD2AcsAhYA3wbKCIWTEcAjxMbnPg94Mvuvj5N/yXSw+I/C4+7++j482eAK9z9TTM7AbjV3T8fH0H9KeAQYj83X3D3lVkqO+cooCTntP5wiIfDE8Do+Bh4mFl/d99uZkXEhvY51d3fNTMnNpTS78zsDuB9d/+hma0Bprp7nZkVJxvg1cz2B/a4+8dmVgo85O7l8RqqgJHE/rLeAPzS3W80s28DR7n7d8xsfrzuRzP1fyM9o93P4IHEhoKqadVkP3cfaWbXEvucbR54915iP6u5NPBu1kRqqCPJW39tDqe4a8zs3PjjwcQGbX0X2EXsKAZgJTAl/vhFYL6ZPUzs6CeZQuC/zGwcsaOtY1ute6l5rDczW0/sr2aIHUmd1q29klzRC6h393EJ1l1C/HqVuy81s+aBd0MdPzMougYlUfBR84P40cwXgBPdvYzYLLd946sbW40p10T8DzR3v4LYxezBwGozSzY/2L8RO41XRmyKkz6t1rUeTX5Pq+d70B+Ckebu7wNvxU8VN083XxZfHYWBd7NGASW56ANiU3UkcjCww913xi9UT+xsY2Z2jLsvd/fvA+/QdpqG9tveEj898y/EBltNV92SI+ID1S4FhptZrZldAswELjGzl4G1fDJL8XXAZfHlDxHgwLsh0192knPi15NejN+H0kDsqKbZk8AVZvYKsWsCy1LY5Nz4NSUDngFeTtLubuA38b+Un6XVkVuKfk1slPBrgPPVSSI3ufuMJKv26nru7q8BkzJbUXSpk4SIiARJp/hERCRIOsUn0o6ZVQC3t1v8lrufm6i9iGSGTvGJiEiQdIpPRESCpIASEZEgKaBERCRICigREQmSAkpERIL0/wGH5xbTl6WZbQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "cols = 'trans_amt'\n",
    "train_tsts.geo_code = train_tsts.geo_code.str[:2]\n",
    "df2 = plot_s(train_tsts, cols)\n",
    "sn.jointplot(data=df2, x=cols, y='tag_sum_cnt')    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>2910</th>\n",
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       "      <th>2901</th>\n",
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       "      <th>4979</th>\n",
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       "      <th>2925</th>\n",
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       "      <th>6044</th>\n",
       "      <td>28470</td>\n",
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       "      <th>10685</th>\n",
       "      <td>708756</td>\n",
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       "      <th>10689</th>\n",
       "      <td>719357</td>\n",
       "      <td>1</td>\n",
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       "      <th>4137</th>\n",
       "      <td>14074</td>\n",
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       "      <th>5539</th>\n",
       "      <td>25358</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4139</th>\n",
       "      <td>14093</td>\n",
       "      <td>1</td>\n",
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       "      <td>1.0</td>\n",
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       "      <th>6714</th>\n",
       "      <td>39232</td>\n",
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       "      <th>6071</th>\n",
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       "      <th>6707</th>\n",
       "      <td>39058</td>\n",
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       "      <th>10976</th>\n",
       "      <td>1370114</td>\n",
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       "      <th>1573</th>\n",
       "      <td>4432</td>\n",
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       "      <th>6692</th>\n",
       "      <td>38696</td>\n",
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       "      <th>4731</th>\n",
       "      <td>18339</td>\n",
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       "      <th>10697</th>\n",
       "      <td>728055</td>\n",
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       "    <tr>\n",
       "      <th>5331</th>\n",
       "      <td>23509</td>\n",
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       "      <th>1570</th>\n",
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       "<p>357 rows × 4 columns</p>\n",
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      ],
      "text/plain": [
       "       trans_amt  tag_cnt  tag_sum  tag_sum_cnt\n",
       "4940       20185        3        3          1.0\n",
       "4939       20182        2        2          1.0\n",
       "9863      191738        2        2          1.0\n",
       "4934       20147        1        1          1.0\n",
       "2879        8551        1        1          1.0\n",
       "7148       48311        1        1          1.0\n",
       "10508     519563        1        1          1.0\n",
       "9893      199893        3        3          1.0\n",
       "4959       20413        1        1          1.0\n",
       "2849        8453        1        1          1.0\n",
       "5196       22357        1        1          1.0\n",
       "4966       20457        3        3          1.0\n",
       "9874      194185        1        1          1.0\n",
       "2910        8673        1        1          1.0\n",
       "2901        8648        1        1          1.0\n",
       "4979       20628        1        1          1.0\n",
       "2925        8719        1        1          1.0\n",
       "2923        8714        1        1          1.0\n",
       "2922        8711        1        1          1.0\n",
       "2921        8708        3        3          1.0\n",
       "9947      215931        3        3          1.0\n",
       "2918        8700        2        2          1.0\n",
       "2917        8697        1        1          1.0\n",
       "1193        3351        1        1          1.0\n",
       "9945      215659        4        4          1.0\n",
       "10486     510125        1        1          1.0\n",
       "5187       22305        1        1          1.0\n",
       "2912        8681        1        1          1.0\n",
       "10491     512496        1        1          1.0\n",
       "8089       63001        2        2          1.0\n",
       "...          ...      ...      ...          ...\n",
       "4636       17467        1        1          1.0\n",
       "4632       17431        2        2          1.0\n",
       "10946    1305418        1        1          1.0\n",
       "8751       75912        1        1          1.0\n",
       "4189       14425        2        2          1.0\n",
       "9476      121009        1        1          1.0\n",
       "4183       14373        1        1          1.0\n",
       "2320        6716        1        1          1.0\n",
       "9548      129490        3        3          1.0\n",
       "4117       13941        2        2          1.0\n",
       "10669     679398        2        2          1.0\n",
       "6044       28470        1        1          1.0\n",
       "4747       18464        1        1          1.0\n",
       "4745       18453        1        1          1.0\n",
       "10685     708756        1        1          1.0\n",
       "10689     719357        1        1          1.0\n",
       "4137       14074        1        1          1.0\n",
       "5539       25358        1        1          1.0\n",
       "4139       14093        1        1          1.0\n",
       "6714       39232        2        2          1.0\n",
       "6071       28843        1        1          1.0\n",
       "6707       39058        1        1          1.0\n",
       "10976    1370114        1        1          1.0\n",
       "1573        4432        1        1          1.0\n",
       "6692       38696        1        1          1.0\n",
       "4731       18339        2        2          1.0\n",
       "10697     728055        1        1          1.0\n",
       "5331       23509        1        1          1.0\n",
       "1570        4424        1        1          1.0\n",
       "4548       16678        1        1          1.0\n",
       "\n",
       "[357 rows x 4 columns]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.tag_sum_cnt == 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.02272727, 0.28571429, 0.5       ])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2[df2.trans_amt > 1370114].tag_sum_cnt.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>1789</td>\n",
       "      <td>144</td>\n",
       "      <td>0.080492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>108</td>\n",
       "      <td>1661</td>\n",
       "      <td>125</td>\n",
       "      <td>0.075256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>110</td>\n",
       "      <td>1303</td>\n",
       "      <td>81</td>\n",
       "      <td>0.062164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>113</td>\n",
       "      <td>1284</td>\n",
       "      <td>133</td>\n",
       "      <td>0.103583</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>116</td>\n",
       "      <td>171</td>\n",
       "      <td>17</td>\n",
       "      <td>0.099415</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>119</td>\n",
       "      <td>236</td>\n",
       "      <td>46</td>\n",
       "      <td>0.194915</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>121</td>\n",
       "      <td>576</td>\n",
       "      <td>29</td>\n",
       "      <td>0.050347</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>124</td>\n",
       "      <td>105</td>\n",
       "      <td>37</td>\n",
       "      <td>0.352381</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>127</td>\n",
       "      <td>2308</td>\n",
       "      <td>319</td>\n",
       "      <td>0.138215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>129</td>\n",
       "      <td>270</td>\n",
       "      <td>36</td>\n",
       "      <td>0.133333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>132</td>\n",
       "      <td>274</td>\n",
       "      <td>46</td>\n",
       "      <td>0.167883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>135</td>\n",
       "      <td>240</td>\n",
       "      <td>47</td>\n",
       "      <td>0.195833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>138</td>\n",
       "      <td>211</td>\n",
       "      <td>38</td>\n",
       "      <td>0.180095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>140</td>\n",
       "      <td>718</td>\n",
       "      <td>54</td>\n",
       "      <td>0.075209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>143</td>\n",
       "      <td>271</td>\n",
       "      <td>32</td>\n",
       "      <td>0.118081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>146</td>\n",
       "      <td>226</td>\n",
       "      <td>33</td>\n",
       "      <td>0.146018</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>148</td>\n",
       "      <td>225</td>\n",
       "      <td>52</td>\n",
       "      <td>0.231111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>151</td>\n",
       "      <td>210</td>\n",
       "      <td>21</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>154</td>\n",
       "      <td>817</td>\n",
       "      <td>44</td>\n",
       "      <td>0.053856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>157</td>\n",
       "      <td>217</td>\n",
       "      <td>27</td>\n",
       "      <td>0.124424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>159</td>\n",
       "      <td>246</td>\n",
       "      <td>26</td>\n",
       "      <td>0.105691</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>162</td>\n",
       "      <td>188</td>\n",
       "      <td>23</td>\n",
       "      <td>0.122340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>165</td>\n",
       "      <td>168</td>\n",
       "      <td>22</td>\n",
       "      <td>0.130952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>167</td>\n",
       "      <td>298</td>\n",
       "      <td>20</td>\n",
       "      <td>0.067114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>170</td>\n",
       "      <td>159</td>\n",
       "      <td>20</td>\n",
       "      <td>0.125786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>173</td>\n",
       "      <td>185</td>\n",
       "      <td>10</td>\n",
       "      <td>0.054054</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>176</td>\n",
       "      <td>307</td>\n",
       "      <td>38</td>\n",
       "      <td>0.123779</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>178</td>\n",
       "      <td>24</td>\n",
       "      <td>2</td>\n",
       "      <td>0.083333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>181</td>\n",
       "      <td>322</td>\n",
       "      <td>28</td>\n",
       "      <td>0.086957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>11195</th>\n",
       "      <td>7067632</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11196</th>\n",
       "      <td>7220495</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11197</th>\n",
       "      <td>7811082</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11198</th>\n",
       "      <td>8154945</td>\n",
       "      <td>9</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11199</th>\n",
       "      <td>8807333</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11200</th>\n",
       "      <td>8970430</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11201</th>\n",
       "      <td>9089626</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11202</th>\n",
       "      <td>9148749</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11203</th>\n",
       "      <td>9160709</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11204</th>\n",
       "      <td>9459720</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11205</th>\n",
       "      <td>9785914</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11206</th>\n",
       "      <td>10057742</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11207</th>\n",
       "      <td>10873227</td>\n",
       "      <td>12</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11208</th>\n",
       "      <td>10920498</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11209</th>\n",
       "      <td>11134182</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11210</th>\n",
       "      <td>11149075</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11211</th>\n",
       "      <td>11272814</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11212</th>\n",
       "      <td>11688711</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11213</th>\n",
       "      <td>11715079</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11214</th>\n",
       "      <td>11743077</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11215</th>\n",
       "      <td>12232368</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11216</th>\n",
       "      <td>12232784</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11217</th>\n",
       "      <td>12235543</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11218</th>\n",
       "      <td>12253837</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11219</th>\n",
       "      <td>12504196</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11220</th>\n",
       "      <td>13586072</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11221</th>\n",
       "      <td>13591509</td>\n",
       "      <td>85</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11222</th>\n",
       "      <td>27182918</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11223</th>\n",
       "      <td>32619481</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11224</th>\n",
       "      <td>111069095</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>11225 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       trans_amt  tag_cnt  tag_sum  tag_sum_cnt\n",
       "0            102    17228    13030     0.756327\n",
       "1            105     1789      144     0.080492\n",
       "2            108     1661      125     0.075256\n",
       "3            110     1303       81     0.062164\n",
       "4            113     1284      133     0.103583\n",
       "5            116      171       17     0.099415\n",
       "6            119      236       46     0.194915\n",
       "7            121      576       29     0.050347\n",
       "8            124      105       37     0.352381\n",
       "9            127     2308      319     0.138215\n",
       "10           129      270       36     0.133333\n",
       "11           132      274       46     0.167883\n",
       "12           135      240       47     0.195833\n",
       "13           138      211       38     0.180095\n",
       "14           140      718       54     0.075209\n",
       "15           143      271       32     0.118081\n",
       "16           146      226       33     0.146018\n",
       "17           148      225       52     0.231111\n",
       "18           151      210       21     0.100000\n",
       "19           154      817       44     0.053856\n",
       "20           157      217       27     0.124424\n",
       "21           159      246       26     0.105691\n",
       "22           162      188       23     0.122340\n",
       "23           165      168       22     0.130952\n",
       "24           167      298       20     0.067114\n",
       "25           170      159       20     0.125786\n",
       "26           173      185       10     0.054054\n",
       "27           176      307       38     0.123779\n",
       "28           178       24        2     0.083333\n",
       "29           181      322       28     0.086957\n",
       "...          ...      ...      ...          ...\n",
       "11195    7067632        2        0     0.000000\n",
       "11196    7220495        2        0     0.000000\n",
       "11197    7811082        2        0     0.000000\n",
       "11198    8154945        9        0     0.000000\n",
       "11199    8807333        1        0     0.000000\n",
       "11200    8970430        2        0     0.000000\n",
       "11201    9089626        2        0     0.000000\n",
       "11202    9148749        2        0     0.000000\n",
       "11203    9160709        1        0     0.000000\n",
       "11204    9459720        6        0     0.000000\n",
       "11205    9785914        1        0     0.000000\n",
       "11206   10057742        2        0     0.000000\n",
       "11207   10873227       12        0     0.000000\n",
       "11208   10920498        2        0     0.000000\n",
       "11209   11134182        1        0     0.000000\n",
       "11210   11149075        2        0     0.000000\n",
       "11211   11272814        2        0     0.000000\n",
       "11212   11688711        1        0     0.000000\n",
       "11213   11715079        1        0     0.000000\n",
       "11214   11743077        1        0     0.000000\n",
       "11215   12232368        1        0     0.000000\n",
       "11216   12232784        2        0     0.000000\n",
       "11217   12235543        2        0     0.000000\n",
       "11218   12253837        2        0     0.000000\n",
       "11219   12504196        1        0     0.000000\n",
       "11220   13586072        1        0     0.000000\n",
       "11221   13591509       85        0     0.000000\n",
       "11222   27182918        2        0     0.000000\n",
       "11223   32619481        5        0     0.000000\n",
       "11224  111069095        2        0     0.000000\n",
       "\n",
       "[11225 rows x 4 columns]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.sort_values('trans_amt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "      <td>qw</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>rb</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>dr</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>dp</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>u1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>wc</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9t</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>6p</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>tw</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>gc</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>yc</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>w5</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>ty</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>tx</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>tv</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>y9</td>\n",
       "      <td>98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>vb</td>\n",
       "      <td>156</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>we</td>\n",
       "      <td>276</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>wj</td>\n",
       "      <td>337</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>wn</td>\n",
       "      <td>352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>wp</td>\n",
       "      <td>376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>y8</td>\n",
       "      <td>777</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>wh</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>wz</td>\n",
       "      <td>958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>w7</td>\n",
       "      <td>1106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>tz</td>\n",
       "      <td>1142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>yb</td>\n",
       "      <td>1286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>wr</td>\n",
       "      <td>2400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>wx</td>\n",
       "      <td>8692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>wk</td>\n",
       "      <td>10082</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>wq</td>\n",
       "      <td>11624</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>ww</td>\n",
       "      <td>15441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>wm</td>\n",
       "      <td>15594</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>ws</td>\n",
       "      <td>19880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>wt</td>\n",
       "      <td>24418</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   geo_code    UID\n",
       "10       tt      1\n",
       "18       u8      1\n",
       "2        c2      1\n",
       "21       w3      1\n",
       "9        tc      1\n",
       "16       u0      2\n",
       "36       wv      2\n",
       "39       wy      2\n",
       "41       y0      2\n",
       "20       w1      2\n",
       "7        r1      2\n",
       "6        qw      2\n",
       "8        rb      2\n",
       "4        dr      3\n",
       "3        dp      3\n",
       "17       u1      4\n",
       "24       wc      6\n",
       "1        9t      7\n",
       "0        6p      8\n",
       "12       tw     10\n",
       "5        gc     12\n",
       "45       yc     15\n",
       "22       w5     37\n",
       "14       ty     39\n",
       "13       tx     69\n",
       "11       tv     82\n",
       "43       y9     98\n",
       "19       vb    156\n",
       "25       we    276\n",
       "27       wj    337\n",
       "30       wn    352\n",
       "31       wp    376\n",
       "42       y8    777\n",
       "26       wh    891\n",
       "40       wz    958\n",
       "23       w7   1106\n",
       "15       tz   1142\n",
       "44       yb   1286\n",
       "33       wr   2400\n",
       "38       wx   8692\n",
       "28       wk  10082\n",
       "32       wq  11624\n",
       "37       ww  15441\n",
       "29       wm  15594\n",
       "34       ws  19880\n",
       "35       wt  24418"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_trans.geo_code = test_trans.geo_code.str[:2]\n",
    "test_trans1 = test_trans.groupby(cols, as_index=False).count()[[cols, 'UID']].sort_values('UID')\n",
    "\n",
    "test_trans1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>UID</th>\n",
       "      <th>channel</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>trans_amt</th>\n",
       "      <th>amt_src1</th>\n",
       "      <th>merchant</th>\n",
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       "      <th>bal</th>\n",
       "      <th>amt_src2</th>\n",
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       "      <th>geo_code</th>\n",
       "      <th>trans_type2</th>\n",
       "      <th>market_code</th>\n",
       "      <th>market_type</th>\n",
       "      <th>ip1_sub</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>ef0652146ed936e4</td>\n",
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       "      <th>1</th>\n",
       "      <td>120992</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>08:27:11</td>\n",
       "      <td>1459</td>\n",
       "      <td>acdbdb842ac20f1e</td>\n",
       "      <td>e36d1d861d5fc9ec</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>100</td>\n",
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       "      <td>wm</td>\n",
       "      <td>102.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>104649</td>\n",
       "      <td>102</td>\n",
       "      <td>24</td>\n",
       "      <td>21:01:20</td>\n",
       "      <td>27282</td>\n",
       "      <td>9451ef3c5a0d6807</td>\n",
       "      <td>e36d1d861d5fc9ec</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6d55c54c8b1056fb</td>\n",
       "      <td>...</td>\n",
       "      <td>c8ee78931469a12f</td>\n",
       "      <td>100</td>\n",
       "      <td>e86309711bd84312</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>278271d87ee64d6d</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>112948</td>\n",
       "      <td>140</td>\n",
       "      <td>2</td>\n",
       "      <td>18:42:28</td>\n",
       "      <td>13691</td>\n",
       "      <td>c5fc631370cabc0d</td>\n",
       "      <td>fc9fc9836e7cf3a1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>705826c957ead9b2</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ws</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1eb204a90e957a89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>101887</td>\n",
       "      <td>140</td>\n",
       "      <td>25</td>\n",
       "      <td>20:43:59</td>\n",
       "      <td>8254</td>\n",
       "      <td>4d7831c6f695ab19</td>\n",
       "      <td>a3ef26d266ae42cc</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c2f2023d279665b2</td>\n",
       "      <td>...</td>\n",
       "      <td>3bb3fd15dab758e5</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>88ebb6d818983fab</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>116921</td>\n",
       "      <td>140</td>\n",
       "      <td>14</td>\n",
       "      <td>23:34:06</td>\n",
       "      <td>915</td>\n",
       "      <td>992d3ce08a4ca702</td>\n",
       "      <td>73c1b9741002c70f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c2f2023d279665b2</td>\n",
       "      <td>...</td>\n",
       "      <td>3fdab50a2463d743</td>\n",
       "      <td>100</td>\n",
       "      <td>a2aa73cdb6621133</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ws</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cf364a1b8791dd34</td>\n",
       "      <td>2.0</td>\n",
       "      <td>a885ca5467b9d883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>123573</td>\n",
       "      <td>102</td>\n",
       "      <td>28</td>\n",
       "      <td>22:00:22</td>\n",
       "      <td>21846</td>\n",
       "      <td>9451ef3c5a0d6807</td>\n",
       "      <td>8f57527418b3f457</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61bfb66c928f36ac</td>\n",
       "      <td>...</td>\n",
       "      <td>0a9e5bdbe6e7dbbb</td>\n",
       "      <td>100</td>\n",
       "      <td>12e06cb81b0fcf2a</td>\n",
       "      <td>7fa2d07833dc174c</td>\n",
       "      <td>91478b0629e56edf</td>\n",
       "      <td>wm</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a2862cb659e3e41f</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>126204</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>11:25:16</td>\n",
       "      <td>1459</td>\n",
       "      <td>acdbdb842ac20f1e</td>\n",
       "      <td>e36d1d861d5fc9ec</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26bcf43a19df14c8</td>\n",
       "      <td>...</td>\n",
       "      <td>d6fb589e9d614a98</td>\n",
       "      <td>100</td>\n",
       "      <td>d46a2a9577fb52c3</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wm</td>\n",
       "      <td>102.0</td>\n",
       "      <td>7e0488b83330d86f</td>\n",
       "      <td>1.0</td>\n",
       "      <td>995d4b5b4169214b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>107815</td>\n",
       "      <td>102</td>\n",
       "      <td>5</td>\n",
       "      <td>07:34:11</td>\n",
       "      <td>2818</td>\n",
       "      <td>9451ef3c5a0d6807</td>\n",
       "      <td>3bd5cf7c40962299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61bfb66c928f36ac</td>\n",
       "      <td>...</td>\n",
       "      <td>4f3673012d08e097</td>\n",
       "      <td>100</td>\n",
       "      <td>12e06cb81b0fcf2a</td>\n",
       "      <td>01e794a77e3f8c53</td>\n",
       "      <td>05c03093d0c9b9b7</td>\n",
       "      <td>wx</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8cb4852ea1b22de8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>131358</td>\n",
       "      <td>102</td>\n",
       "      <td>13</td>\n",
       "      <td>11:54:37</td>\n",
       "      <td>2818</td>\n",
       "      <td>155c9e1c32bd0fa2</td>\n",
       "      <td>3bd5cf7c40962299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61bfb66c928f36ac</td>\n",
       "      <td>...</td>\n",
       "      <td>09075d7bd712e0da</td>\n",
       "      <td>100</td>\n",
       "      <td>cf6e3a074407c379</td>\n",
       "      <td>5d8ce28021e89c17</td>\n",
       "      <td>4172a713605d9d3f</td>\n",
       "      <td>ws</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>65755daf517e70f6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>118264</td>\n",
       "      <td>140</td>\n",
       "      <td>17</td>\n",
       "      <td>03:59:30</td>\n",
       "      <td>10973</td>\n",
       "      <td>4d7831c6f695ab19</td>\n",
       "      <td>c71c876b8979028e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c2f2023d279665b2</td>\n",
       "      <td>...</td>\n",
       "      <td>0b5d0edf2b825e2c</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c8897fd1556a7dc8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>121411</td>\n",
       "      <td>140</td>\n",
       "      <td>4</td>\n",
       "      <td>21:48:34</td>\n",
       "      <td>28043</td>\n",
       "      <td>27c42480134c0d02</td>\n",
       "      <td>c71c876b8979028e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c2f2023d279665b2</td>\n",
       "      <td>...</td>\n",
       "      <td>1c7e2894b8f1c156</td>\n",
       "      <td>100</td>\n",
       "      <td>eb41d44c679aca42</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ws</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ae6b3c837dc15700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>112985</td>\n",
       "      <td>102</td>\n",
       "      <td>16</td>\n",
       "      <td>13:48:13</td>\n",
       "      <td>2818</td>\n",
       "      <td>155c9e1c32bd0fa2</td>\n",
       "      <td>8f57527418b3f457</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61bfb66c928f36ac</td>\n",
       "      <td>...</td>\n",
       "      <td>3f5a2a7a756dc3dc</td>\n",
       "      <td>100</td>\n",
       "      <td>cf6e3a074407c379</td>\n",
       "      <td>e29d9d87630b041d</td>\n",
       "      <td>81227f08c1cab92f</td>\n",
       "      <td>wq</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e366482f76ecaa90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>118540</td>\n",
       "      <td>102</td>\n",
       "      <td>14</td>\n",
       "      <td>19:35:27</td>\n",
       "      <td>290</td>\n",
       "      <td>acdbdb842ac20f1e</td>\n",
       "      <td>931f028aeaaaef6b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>26bcf43a19df14c8</td>\n",
       "      <td>...</td>\n",
       "      <td>06c5bb50f4529b63</td>\n",
       "      <td>100</td>\n",
       "      <td>12e06cb81b0fcf2a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>102.0</td>\n",
       "      <td>4f76c6bd31a54b12</td>\n",
       "      <td>1.0</td>\n",
       "      <td>798e098f59961004</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>118663</td>\n",
       "      <td>102</td>\n",
       "      <td>1</td>\n",
       "      <td>10:29:56</td>\n",
       "      <td>1459</td>\n",
       "      <td>155c9e1c32bd0fa2</td>\n",
       "      <td>3bd5cf7c40962299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61bfb66c928f36ac</td>\n",
       "      <td>...</td>\n",
       "      <td>3ca2cb1f9ae7e593</td>\n",
       "      <td>100</td>\n",
       "      <td>cf6e3a074407c379</td>\n",
       "      <td>702a6354239ba723</td>\n",
       "      <td>d0d31d35e0b013a2</td>\n",
       "      <td>wk</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0a0e63f0fd4ef5db</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>105227</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>19:03:30</td>\n",
       "      <td>12332</td>\n",
       "      <td>c5fc631370cabc0d</td>\n",
       "      <td>fc9fc9836e7cf3a1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>ccf17ebbedabb968</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3bc52616bc739269</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>114165</td>\n",
       "      <td>102</td>\n",
       "      <td>27</td>\n",
       "      <td>18:24:00</td>\n",
       "      <td>23028</td>\n",
       "      <td>155c9e1c32bd0fa2</td>\n",
       "      <td>3bd5cf7c40962299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e0d7b8768da99dd4</td>\n",
       "      <td>...</td>\n",
       "      <td>d68fb86faf9475e5</td>\n",
       "      <td>100</td>\n",
       "      <td>cf6e3a074407c379</td>\n",
       "      <td>41cd3f18dc2ef413</td>\n",
       "      <td>39c12597958a603d</td>\n",
       "      <td>wx</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>d7f4a41055c2bcf7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>107550</td>\n",
       "      <td>140</td>\n",
       "      <td>24</td>\n",
       "      <td>00:31:47</td>\n",
       "      <td>11000</td>\n",
       "      <td>c5fc631370cabc0d</td>\n",
       "      <td>c71c876b8979028e</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c2f2023d279665b2</td>\n",
       "      <td>...</td>\n",
       "      <td>fd30c6ac5ae85f1b</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ws</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>08383b3d43a8afe1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>119869</td>\n",
       "      <td>140</td>\n",
       "      <td>17</td>\n",
       "      <td>17:43:05</td>\n",
       "      <td>6895</td>\n",
       "      <td>c5fc631370cabc0d</td>\n",
       "      <td>fc9fc9836e7cf3a1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>166e23ec116ef2ec</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ws</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>359ce40cd4ef8942</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>121786</td>\n",
       "      <td>140</td>\n",
       "      <td>8</td>\n",
       "      <td>15:46:53</td>\n",
       "      <td>371</td>\n",
       "      <td>f29829bc82459191</td>\n",
       "      <td>fc9fc9836e7cf3a1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>f44e36b8c5b62aee</td>\n",
       "      <td>100</td>\n",
       "      <td>9a8ee16bde15e38a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ws</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>bb62d2a3b9830281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>110554</td>\n",
       "      <td>102</td>\n",
       "      <td>8</td>\n",
       "      <td>10:07:13</td>\n",
       "      <td>28098</td>\n",
       "      <td>9451ef3c5a0d6807</td>\n",
       "      <td>3bd5cf7c40962299</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>61bfb66c928f36ac</td>\n",
       "      <td>...</td>\n",
       "      <td>d93a9f6cbdbc222e</td>\n",
       "      <td>100</td>\n",
       "      <td>12e06cb81b0fcf2a</td>\n",
       "      <td>3d4399c427b37219</td>\n",
       "      <td>0b0bbdea98566fba</td>\n",
       "      <td>tz</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>34b9b02ef183447b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>108420</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>23:22:43</td>\n",
       "      <td>54465</td>\n",
       "      <td>c5fc631370cabc0d</td>\n",
       "      <td>fc9fc9836e7cf3a1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>bdc8a83fabddaff6</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>58d065bcd14939f4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>102303</td>\n",
       "      <td>140</td>\n",
       "      <td>28</td>\n",
       "      <td>09:29:15</td>\n",
       "      <td>2818</td>\n",
       "      <td>c5fc631370cabc0d</td>\n",
       "      <td>92cf8c06a9706155</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c2f2023d279665b2</td>\n",
       "      <td>...</td>\n",
       "      <td>b65e88b4a6f9b309</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c1dab65279348cfa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>104304</td>\n",
       "      <td>140</td>\n",
       "      <td>20</td>\n",
       "      <td>12:09:55</td>\n",
       "      <td>5536</td>\n",
       "      <td>c5fc631370cabc0d</td>\n",
       "      <td>fc9fc9836e7cf3a1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>f87addfecb9302f7</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5a6e0d7c39c7ec11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>124059</td>\n",
       "      <td>102</td>\n",
       "      <td>6</td>\n",
       "      <td>10:27:44</td>\n",
       "      <td>31360</td>\n",
       "      <td>9451ef3c5a0d6807</td>\n",
       "      <td>3f6d3d0f42519ea4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6d55c54c8b1056fb</td>\n",
       "      <td>...</td>\n",
       "      <td>47b7a0ae6a359dc0</td>\n",
       "      <td>100</td>\n",
       "      <td>8d2ff85b6fd5dc78</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wq</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5a6d7444d68667bb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>110306</td>\n",
       "      <td>140</td>\n",
       "      <td>1</td>\n",
       "      <td>11:42:03</td>\n",
       "      <td>7928</td>\n",
       "      <td>c5fc631370cabc0d</td>\n",
       "      <td>fa9d1d0793644daa</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>df19b2c4e3f7c296</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wx</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>dd674104665c980a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>123960</td>\n",
       "      <td>140</td>\n",
       "      <td>2</td>\n",
       "      <td>08:33:58</td>\n",
       "      <td>27282</td>\n",
       "      <td>a571c7fda8b7df37</td>\n",
       "      <td>fc9fc9836e7cf3a1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>f46c16997ad556c3</td>\n",
       "      <td>100</td>\n",
       "      <td>fbf6bf3c8927414c</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wq</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>af57a162b13515c5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>111571</td>\n",
       "      <td>102</td>\n",
       "      <td>16</td>\n",
       "      <td>11:01:03</td>\n",
       "      <td>1459</td>\n",
       "      <td>9451ef3c5a0d6807</td>\n",
       "      <td>8ccf4ac3df6b191f</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6d55c54c8b1056fb</td>\n",
       "      <td>...</td>\n",
       "      <td>b9e22c70d172034f</td>\n",
       "      <td>100</td>\n",
       "      <td>e98d66cbb6c099b5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wm</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5567042e7e4db835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>119275</td>\n",
       "      <td>140</td>\n",
       "      <td>28</td>\n",
       "      <td>19:45:39</td>\n",
       "      <td>4177</td>\n",
       "      <td>a571c7fda8b7df37</td>\n",
       "      <td>37aabcf9f6cdaba5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c2f2023d279665b2</td>\n",
       "      <td>...</td>\n",
       "      <td>e5fede7c5850c793</td>\n",
       "      <td>100</td>\n",
       "      <td>e2a24888724d3e89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ws</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>78a0fecec7c514d8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>113556</td>\n",
       "      <td>140</td>\n",
       "      <td>10</td>\n",
       "      <td>16:06:51</td>\n",
       "      <td>102</td>\n",
       "      <td>4d7831c6f695ab19</td>\n",
       "      <td>59829254a5d8c342</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c2f2023d279665b2</td>\n",
       "      <td>...</td>\n",
       "      <td>17a56d49e3241fc1</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ac809f5acd04178a</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128352</th>\n",
       "      <td>127923</td>\n",
       "      <td>140</td>\n",
       "      <td>16</td>\n",
       "      <td>21:12:33</td>\n",
       "      <td>371</td>\n",
       "      <td>f29829bc82459191</td>\n",
       "      <td>fc9fc9836e7cf3a1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>d0b3cbfe6719ed07</td>\n",
       "      <td>100</td>\n",
       "      <td>9a8ee16bde15e38a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>ws</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>43959476721f93fa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128353</th>\n",
       "      <td>114254</td>\n",
       "      <td>140</td>\n",
       "      <td>16</td>\n",
       "      <td>21:27:53</td>\n",
       "      <td>54465</td>\n",
       "      <td>c5fc631370cabc0d</td>\n",
       "      <td>fc9fc9836e7cf3a1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>c9933250d8525724</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>128357</th>\n",
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       "    <tr>\n",
       "      <th>128358</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>128360</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
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       "      <td>NaN</td>\n",
       "      <td>c2f2023d279665b2</td>\n",
       "      <td>...</td>\n",
       "      <td>bb8c2c47f052533e</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>11112cd8ffedf099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128375</th>\n",
       "      <td>100052</td>\n",
       "      <td>140</td>\n",
       "      <td>22</td>\n",
       "      <td>08:13:35</td>\n",
       "      <td>5536</td>\n",
       "      <td>4d7831c6f695ab19</td>\n",
       "      <td>f11a13ddaefe1c1b</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c2f2023d279665b2</td>\n",
       "      <td>...</td>\n",
       "      <td>152cc2aacdff00e4</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4d38e564aa1a0a01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128376</th>\n",
       "      <td>127747</td>\n",
       "      <td>102</td>\n",
       "      <td>25</td>\n",
       "      <td>17:01:04</td>\n",
       "      <td>915</td>\n",
       "      <td>155c9e1c32bd0fa2</td>\n",
       "      <td>e36d1d861d5fc9ec</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6d55c54c8b1056fb</td>\n",
       "      <td>...</td>\n",
       "      <td>23f3b53a13a48c1b</td>\n",
       "      <td>100</td>\n",
       "      <td>9a8ee16bde15e38a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>85268bc70a64059b</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128377</th>\n",
       "      <td>110883</td>\n",
       "      <td>140</td>\n",
       "      <td>16</td>\n",
       "      <td>16:55:14</td>\n",
       "      <td>1459</td>\n",
       "      <td>4d7831c6f695ab19</td>\n",
       "      <td>fc9fc9836e7cf3a1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>970d523b8f1d682c</td>\n",
       "      <td>100</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wt</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>8b1dea1d78898e08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128378</th>\n",
       "      <td>107139</td>\n",
       "      <td>102</td>\n",
       "      <td>6</td>\n",
       "      <td>17:34:48</td>\n",
       "      <td>27282</td>\n",
       "      <td>155c9e1c32bd0fa2</td>\n",
       "      <td>1e70ea89a4cbb3fe</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>a19e7a8951e54c06</td>\n",
       "      <td>...</td>\n",
       "      <td>e71c7f41a1dd652f</td>\n",
       "      <td>30422</td>\n",
       "      <td>9fefed0a981dcb7a</td>\n",
       "      <td>285b6355aa441a7f</td>\n",
       "      <td>471da700d52afba6</td>\n",
       "      <td>wt</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>d4a48ef47816a56e</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128379</th>\n",
       "      <td>115159</td>\n",
       "      <td>102</td>\n",
       "      <td>3</td>\n",
       "      <td>18:03:42</td>\n",
       "      <td>643</td>\n",
       "      <td>9451ef3c5a0d6807</td>\n",
       "      <td>3f6d3d0f42519ea4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6d55c54c8b1056fb</td>\n",
       "      <td>...</td>\n",
       "      <td>9f9701a114dde21d</td>\n",
       "      <td>100</td>\n",
       "      <td>1ca672c7cb34af43</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wq</td>\n",
       "      <td>102.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>b09c55c649d39617</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128380</th>\n",
       "      <td>103596</td>\n",
       "      <td>140</td>\n",
       "      <td>3</td>\n",
       "      <td>09:52:15</td>\n",
       "      <td>5536</td>\n",
       "      <td>f29829bc82459191</td>\n",
       "      <td>8ebfcc07d7ca0968</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>c2f2023d279665b2</td>\n",
       "      <td>...</td>\n",
       "      <td>77bed09fc1e24565</td>\n",
       "      <td>100</td>\n",
       "      <td>9a8ee16bde15e38a</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wx</td>\n",
       "      <td>105.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2c4d1c9ebfedc565</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128381</th>\n",
       "      <td>118305</td>\n",
       "      <td>140</td>\n",
       "      <td>18</td>\n",
       "      <td>17:30:51</td>\n",
       "      <td>3633</td>\n",
       "      <td>a571c7fda8b7df37</td>\n",
       "      <td>fc9fc9836e7cf3a1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>768160899ae359f6</td>\n",
       "      <td>...</td>\n",
       "      <td>89730fcd11bc97e9</td>\n",
       "      <td>100</td>\n",
       "      <td>e2a24888724d3e89</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>wm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>b7aba2e726d0b518</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>128382 rows × 27 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           UID  channel  day      time  trans_amt          amt_src1  \\\n",
       "0       122913      140    2  09:20:17       8254  a571c7fda8b7df37   \n",
       "1       120992      102    1  08:27:11       1459  acdbdb842ac20f1e   \n",
       "2       104649      102   24  21:01:20      27282  9451ef3c5a0d6807   \n",
       "3       112948      140    2  18:42:28      13691  c5fc631370cabc0d   \n",
       "4       101887      140   25  20:43:59       8254  4d7831c6f695ab19   \n",
       "5       116921      140   14  23:34:06        915  992d3ce08a4ca702   \n",
       "6       123573      102   28  22:00:22      21846  9451ef3c5a0d6807   \n",
       "7       126204      102    1  11:25:16       1459  acdbdb842ac20f1e   \n",
       "8       107815      102    5  07:34:11       2818  9451ef3c5a0d6807   \n",
       "9       131358      102   13  11:54:37       2818  155c9e1c32bd0fa2   \n",
       "10      118264      140   17  03:59:30      10973  4d7831c6f695ab19   \n",
       "11      121411      140    4  21:48:34      28043  27c42480134c0d02   \n",
       "12      112985      102   16  13:48:13       2818  155c9e1c32bd0fa2   \n",
       "13      118540      102   14  19:35:27        290  acdbdb842ac20f1e   \n",
       "14      118663      102    1  10:29:56       1459  155c9e1c32bd0fa2   \n",
       "15      105227      140    3  19:03:30      12332  c5fc631370cabc0d   \n",
       "16      114165      102   27  18:24:00      23028  155c9e1c32bd0fa2   \n",
       "17      107550      140   24  00:31:47      11000  c5fc631370cabc0d   \n",
       "18      119869      140   17  17:43:05       6895  c5fc631370cabc0d   \n",
       "19      121786      140    8  15:46:53        371  f29829bc82459191   \n",
       "20      110554      102    8  10:07:13      28098  9451ef3c5a0d6807   \n",
       "21      108420      140    3  23:22:43      54465  c5fc631370cabc0d   \n",
       "22      102303      140   28  09:29:15       2818  c5fc631370cabc0d   \n",
       "23      104304      140   20  12:09:55       5536  c5fc631370cabc0d   \n",
       "24      124059      102    6  10:27:44      31360  9451ef3c5a0d6807   \n",
       "25      110306      140    1  11:42:03       7928  c5fc631370cabc0d   \n",
       "26      123960      140    2  08:33:58      27282  a571c7fda8b7df37   \n",
       "27      111571      102   16  11:01:03       1459  9451ef3c5a0d6807   \n",
       "28      119275      140   28  19:45:39       4177  a571c7fda8b7df37   \n",
       "29      113556      140   10  16:06:51        102  4d7831c6f695ab19   \n",
       "...        ...      ...  ...       ...        ...               ...   \n",
       "128352  127923      140   16  21:12:33        371  f29829bc82459191   \n",
       "128353  114254      140   16  21:27:53      54465  c5fc631370cabc0d   \n",
       "128354  104079      102    2  08:19:44      54465  9451ef3c5a0d6807   \n",
       "128355  102445      102   10  11:47:47        371  9451ef3c5a0d6807   \n",
       "128356  101535      140   15  10:08:49       4177  4d7831c6f695ab19   \n",
       "128357  120560      102    7  19:01:21      27282  9451ef3c5a0d6807   \n",
       "128358  130277      140    8  11:14:26      54465  f29829bc82459191   \n",
       "128359  122201      102   17  20:45:02       7847  9451ef3c5a0d6807   \n",
       "128360  103310      102   25  17:43:48        915  155c9e1c32bd0fa2   \n",
       "128361  122988      140   29  09:40:50        643  a571c7fda8b7df37   \n",
       "128362  123460      140   25  19:54:12      21302  c5fc631370cabc0d   \n",
       "128363  126507      140   19  12:36:36      13691  c5fc631370cabc0d   \n",
       "128364  103062      140    7  18:45:29        643  4d7831c6f695ab19   \n",
       "128365  130814      102   16  18:08:18        915  155c9e1c32bd0fa2   \n",
       "128366  106797      140   24  09:39:42     539950  27c42480134c0d02   \n",
       "128367  119733      102   10  12:19:40      27282  9451ef3c5a0d6807   \n",
       "128368  111180      140    3  09:05:43      13691  c5fc631370cabc0d   \n",
       "128369  104737      140   14  16:55:19       2818  c5fc631370cabc0d   \n",
       "128370  109795      102   23  10:56:43       2818  acdbdb842ac20f1e   \n",
       "128371  105423      119    9  16:30:12    2718381  8c753ae7afb60e61   \n",
       "128372  105697      102   27  15:29:56     271928  9451ef3c5a0d6807   \n",
       "128373  113863      102   14  15:45:54      14664  9451ef3c5a0d6807   \n",
       "128374  119029      140   14  11:21:18        371  c5fc631370cabc0d   \n",
       "128375  100052      140   22  08:13:35       5536  4d7831c6f695ab19   \n",
       "128376  127747      102   25  17:01:04        915  155c9e1c32bd0fa2   \n",
       "128377  110883      140   16  16:55:14       1459  4d7831c6f695ab19   \n",
       "128378  107139      102    6  17:34:48      27282  155c9e1c32bd0fa2   \n",
       "128379  115159      102    3  18:03:42        643  9451ef3c5a0d6807   \n",
       "128380  103596      140    3  09:52:15       5536  f29829bc82459191   \n",
       "128381  118305      140   18  17:30:51       3633  a571c7fda8b7df37   \n",
       "\n",
       "                merchant             code1 code2       trans_type1  \\\n",
       "0       fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "1       e36d1d861d5fc9ec               NaN   NaN  26bcf43a19df14c8   \n",
       "2       e36d1d861d5fc9ec               NaN   NaN  6d55c54c8b1056fb   \n",
       "3       fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "4       a3ef26d266ae42cc               NaN   NaN  c2f2023d279665b2   \n",
       "5       73c1b9741002c70f               NaN   NaN  c2f2023d279665b2   \n",
       "6       8f57527418b3f457               NaN   NaN  61bfb66c928f36ac   \n",
       "7       e36d1d861d5fc9ec               NaN   NaN  26bcf43a19df14c8   \n",
       "8       3bd5cf7c40962299               NaN   NaN  61bfb66c928f36ac   \n",
       "9       3bd5cf7c40962299               NaN   NaN  61bfb66c928f36ac   \n",
       "10      c71c876b8979028e               NaN   NaN  c2f2023d279665b2   \n",
       "11      c71c876b8979028e               NaN   NaN  c2f2023d279665b2   \n",
       "12      8f57527418b3f457               NaN   NaN  61bfb66c928f36ac   \n",
       "13      931f028aeaaaef6b               NaN   NaN  26bcf43a19df14c8   \n",
       "14      3bd5cf7c40962299               NaN   NaN  61bfb66c928f36ac   \n",
       "15      fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "16      3bd5cf7c40962299               NaN   NaN  e0d7b8768da99dd4   \n",
       "17      c71c876b8979028e               NaN   NaN  c2f2023d279665b2   \n",
       "18      fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "19      fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "20      3bd5cf7c40962299               NaN   NaN  61bfb66c928f36ac   \n",
       "21      fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "22      92cf8c06a9706155               NaN   NaN  c2f2023d279665b2   \n",
       "23      fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "24      3f6d3d0f42519ea4               NaN   NaN  6d55c54c8b1056fb   \n",
       "25      fa9d1d0793644daa               NaN   NaN  768160899ae359f6   \n",
       "26      fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "27      8ccf4ac3df6b191f               NaN   NaN  6d55c54c8b1056fb   \n",
       "28      37aabcf9f6cdaba5               NaN   NaN  c2f2023d279665b2   \n",
       "29      59829254a5d8c342               NaN   NaN  c2f2023d279665b2   \n",
       "...                  ...               ...   ...               ...   \n",
       "128352  fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "128353  fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "128354  3f6d3d0f42519ea4               NaN   NaN  6d55c54c8b1056fb   \n",
       "128355  fafb0e565bc51007               NaN   NaN  6d55c54c8b1056fb   \n",
       "128356  922f93eb30110132               NaN   NaN  c2f2023d279665b2   \n",
       "128357  3defe35707a2c8a4               NaN   NaN  6d55c54c8b1056fb   \n",
       "128358  46d8f146a3c8a8bf               NaN   NaN  c2f2023d279665b2   \n",
       "128359  d70112da701d01ec               NaN   NaN  6d55c54c8b1056fb   \n",
       "128360  e36d1d861d5fc9ec               NaN   NaN  6d55c54c8b1056fb   \n",
       "128361  ef8b3d0fc5d5cbfa               NaN   NaN  c2f2023d279665b2   \n",
       "128362  fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "128363  fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "128364  cd257fa456b1ffd7               NaN   NaN  c2f2023d279665b2   \n",
       "128365  e36d1d861d5fc9ec               NaN   NaN  6d55c54c8b1056fb   \n",
       "128366  066c907b0b66fec5  066c907b0b66fec5   NaN  c2f2023d279665b2   \n",
       "128367  3f6d3d0f42519ea4               NaN   NaN  6d55c54c8b1056fb   \n",
       "128368  fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "128369  af50e11cc5884eb9               NaN   NaN  c2f2023d279665b2   \n",
       "128370  e36d1d861d5fc9ec               NaN   NaN  26bcf43a19df14c8   \n",
       "128371  7051de5689e83caa               NaN   NaN  85bced5214d33ad2   \n",
       "128372  1e70ea89a4cbb3fe               NaN   NaN  a19e7a8951e54c06   \n",
       "128373  e36d1d861d5fc9ec               NaN   NaN  6d55c54c8b1056fb   \n",
       "128374  904538d108065957               NaN   NaN  c2f2023d279665b2   \n",
       "128375  f11a13ddaefe1c1b               NaN   NaN  c2f2023d279665b2   \n",
       "128376  e36d1d861d5fc9ec               NaN   NaN  6d55c54c8b1056fb   \n",
       "128377  fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "128378  1e70ea89a4cbb3fe               NaN   NaN  a19e7a8951e54c06   \n",
       "128379  3f6d3d0f42519ea4               NaN   NaN  6d55c54c8b1056fb   \n",
       "128380  8ebfcc07d7ca0968               NaN   NaN  c2f2023d279665b2   \n",
       "128381  fc9fc9836e7cf3a1               NaN   NaN  768160899ae359f6   \n",
       "\n",
       "              ...                      ip1    bal          amt_src2  \\\n",
       "0             ...         f15bed7acee13415    100  a9edadc983f95061   \n",
       "1             ...         1f004929873b0d95    100  1ca672c7cb34af43   \n",
       "2             ...         c8ee78931469a12f    100  e86309711bd84312   \n",
       "3             ...         705826c957ead9b2    100               NaN   \n",
       "4             ...         3bb3fd15dab758e5    100               NaN   \n",
       "5             ...         3fdab50a2463d743    100  a2aa73cdb6621133   \n",
       "6             ...         0a9e5bdbe6e7dbbb    100  12e06cb81b0fcf2a   \n",
       "7             ...         d6fb589e9d614a98    100  d46a2a9577fb52c3   \n",
       "8             ...         4f3673012d08e097    100  12e06cb81b0fcf2a   \n",
       "9             ...         09075d7bd712e0da    100  cf6e3a074407c379   \n",
       "10            ...         0b5d0edf2b825e2c    100               NaN   \n",
       "11            ...         1c7e2894b8f1c156    100  eb41d44c679aca42   \n",
       "12            ...         3f5a2a7a756dc3dc    100  cf6e3a074407c379   \n",
       "13            ...         06c5bb50f4529b63    100  12e06cb81b0fcf2a   \n",
       "14            ...         3ca2cb1f9ae7e593    100  cf6e3a074407c379   \n",
       "15            ...         ccf17ebbedabb968    100               NaN   \n",
       "16            ...         d68fb86faf9475e5    100  cf6e3a074407c379   \n",
       "17            ...         fd30c6ac5ae85f1b    100               NaN   \n",
       "18            ...         166e23ec116ef2ec    100               NaN   \n",
       "19            ...         f44e36b8c5b62aee    100  9a8ee16bde15e38a   \n",
       "20            ...         d93a9f6cbdbc222e    100  12e06cb81b0fcf2a   \n",
       "21            ...         bdc8a83fabddaff6    100               NaN   \n",
       "22            ...         b65e88b4a6f9b309    100               NaN   \n",
       "23            ...         f87addfecb9302f7    100               NaN   \n",
       "24            ...         47b7a0ae6a359dc0    100  8d2ff85b6fd5dc78   \n",
       "25            ...         df19b2c4e3f7c296    100               NaN   \n",
       "26            ...         f46c16997ad556c3    100  fbf6bf3c8927414c   \n",
       "27            ...         b9e22c70d172034f    100  e98d66cbb6c099b5   \n",
       "28            ...         e5fede7c5850c793    100  e2a24888724d3e89   \n",
       "29            ...         17a56d49e3241fc1    100               NaN   \n",
       "...           ...                      ...    ...               ...   \n",
       "128352        ...         d0b3cbfe6719ed07    100  9a8ee16bde15e38a   \n",
       "128353        ...         c9933250d8525724    100               NaN   \n",
       "128354        ...         35d1db00590584a7    100  e98d66cbb6c099b5   \n",
       "128355        ...         ee364e37e288f95f    100  1ca672c7cb34af43   \n",
       "128356        ...         5e1026d4428e4001    100               NaN   \n",
       "128357        ...         32cb7ddb66e81b21    100  1ca672c7cb34af43   \n",
       "128358        ...         94910da61d13dc26    100  9a8ee16bde15e38a   \n",
       "128359        ...         8e6ce6a9bc96c0a3    100  e98d66cbb6c099b5   \n",
       "128360        ...         aa9e21204c40948c    100  9a8ee16bde15e38a   \n",
       "128361        ...         2198c65c1fdba2d0    100  e7bed445b4f874ac   \n",
       "128362        ...         2eeada04d8522081    100               NaN   \n",
       "128363        ...         f3a4ce9ad31b75e4    100               NaN   \n",
       "128364        ...         f37be5bae57110ed    100               NaN   \n",
       "128365        ...         fdd5ed61037fed73  52948  9a8ee16bde15e38a   \n",
       "128366        ...         39e7d379f804631a    100  974bf14359ae6ea1   \n",
       "128367        ...         08a5425c746dc669    100  7bbedaeb92ef822e   \n",
       "128368        ...         19781c4335b4340a    100               NaN   \n",
       "128369        ...         1078344196523325    100               NaN   \n",
       "128370        ...         78ff22af95679a5a    100  2f60693e4248d7d2   \n",
       "128371        ...         15dbd79e31c584b9    100  e98d66cbb6c099b5   \n",
       "128372        ...         f9ce032a83fc8eb8    100  1ca672c7cb34af43   \n",
       "128373        ...         7f023690230e1398    100  2f60693e4248d7d2   \n",
       "128374        ...         bb8c2c47f052533e    100               NaN   \n",
       "128375        ...         152cc2aacdff00e4    100               NaN   \n",
       "128376        ...         23f3b53a13a48c1b    100  9a8ee16bde15e38a   \n",
       "128377        ...         970d523b8f1d682c    100               NaN   \n",
       "128378        ...         e71c7f41a1dd652f  30422  9fefed0a981dcb7a   \n",
       "128379        ...         9f9701a114dde21d    100  1ca672c7cb34af43   \n",
       "128380        ...         77bed09fc1e24565    100  9a8ee16bde15e38a   \n",
       "128381        ...         89730fcd11bc97e9    100  e2a24888724d3e89   \n",
       "\n",
       "                 acc_id2           acc_id3 geo_code trans_type2  \\\n",
       "0                    NaN               NaN       wk         NaN   \n",
       "1                    NaN               NaN       wm       102.0   \n",
       "2                    NaN               NaN       wt       102.0   \n",
       "3                    NaN               NaN       ws         NaN   \n",
       "4                    NaN               NaN      NaN       105.0   \n",
       "5                    NaN               NaN       ws         NaN   \n",
       "6       7fa2d07833dc174c  91478b0629e56edf       wm       102.0   \n",
       "7                    NaN               NaN       wm       102.0   \n",
       "8       01e794a77e3f8c53  05c03093d0c9b9b7       wx       102.0   \n",
       "9       5d8ce28021e89c17  4172a713605d9d3f       ws       102.0   \n",
       "10                   NaN               NaN      NaN         NaN   \n",
       "11                   NaN               NaN       ws         NaN   \n",
       "12      e29d9d87630b041d  81227f08c1cab92f       wq       102.0   \n",
       "13                   NaN               NaN      NaN       102.0   \n",
       "14      702a6354239ba723  d0d31d35e0b013a2       wk       102.0   \n",
       "15                   NaN               NaN       wq         NaN   \n",
       "16      41cd3f18dc2ef413  39c12597958a603d       wx       102.0   \n",
       "17                   NaN               NaN       ws         NaN   \n",
       "18                   NaN               NaN       ws         NaN   \n",
       "19                   NaN               NaN       ws         NaN   \n",
       "20      3d4399c427b37219  0b0bbdea98566fba       tz       102.0   \n",
       "21                   NaN               NaN       wm         NaN   \n",
       "22                   NaN               NaN       wt       105.0   \n",
       "23                   NaN               NaN       wt         NaN   \n",
       "24                   NaN               NaN       wq       102.0   \n",
       "25                   NaN               NaN       wx         NaN   \n",
       "26                   NaN               NaN       wq         NaN   \n",
       "27                   NaN               NaN       wm       102.0   \n",
       "28                   NaN               NaN       ws       105.0   \n",
       "29                   NaN               NaN       wt         NaN   \n",
       "...                  ...               ...      ...         ...   \n",
       "128352               NaN               NaN       ws         NaN   \n",
       "128353               NaN               NaN       ws         NaN   \n",
       "128354               NaN               NaN       wt       102.0   \n",
       "128355               NaN               NaN      NaN       102.0   \n",
       "128356               NaN               NaN       ws       105.0   \n",
       "128357               NaN               NaN       wq       102.0   \n",
       "128358               NaN               NaN       wm       105.0   \n",
       "128359               NaN               NaN      NaN       105.0   \n",
       "128360               NaN               NaN       wt       102.0   \n",
       "128361               NaN               NaN       wt         NaN   \n",
       "128362               NaN               NaN       ww         NaN   \n",
       "128363               NaN               NaN       wt         NaN   \n",
       "128364               NaN               NaN      NaN       105.0   \n",
       "128365               NaN               NaN       ws       102.0   \n",
       "128366               NaN               NaN       wm       104.0   \n",
       "128367               NaN               NaN       wt       102.0   \n",
       "128368               NaN               NaN       wt         NaN   \n",
       "128369               NaN               NaN       wm       105.0   \n",
       "128370               NaN               NaN       ws       102.0   \n",
       "128371               NaN  d29d30b0938fff9b      NaN         NaN   \n",
       "128372  8f5c3e02d7a6a8a4  6898703924e6fee4       wk       102.0   \n",
       "128373               NaN               NaN       wm       102.0   \n",
       "128374               NaN               NaN       wm         NaN   \n",
       "128375               NaN               NaN      NaN       105.0   \n",
       "128376               NaN               NaN       wt       102.0   \n",
       "128377               NaN               NaN       wt         NaN   \n",
       "128378  285b6355aa441a7f  471da700d52afba6       wt       102.0   \n",
       "128379               NaN               NaN       wq       102.0   \n",
       "128380               NaN               NaN       wx       105.0   \n",
       "128381               NaN               NaN       wm         NaN   \n",
       "\n",
       "             market_code  market_type           ip1_sub  \n",
       "0                    NaN          NaN  ef0652146ed936e4  \n",
       "1       2ecf94369847c748          1.0  659b0c7fc818ff5e  \n",
       "2                    NaN          NaN  278271d87ee64d6d  \n",
       "3                    NaN          NaN  1eb204a90e957a89  \n",
       "4                    NaN          NaN  88ebb6d818983fab  \n",
       "5       cf364a1b8791dd34          2.0  a885ca5467b9d883  \n",
       "6                    NaN          NaN  a2862cb659e3e41f  \n",
       "7       7e0488b83330d86f          1.0  995d4b5b4169214b  \n",
       "8                    NaN          NaN  8cb4852ea1b22de8  \n",
       "9                    NaN          NaN  65755daf517e70f6  \n",
       "10                   NaN          NaN  c8897fd1556a7dc8  \n",
       "11                   NaN          NaN  ae6b3c837dc15700  \n",
       "12                   NaN          NaN  e366482f76ecaa90  \n",
       "13      4f76c6bd31a54b12          1.0  798e098f59961004  \n",
       "14                   NaN          NaN  0a0e63f0fd4ef5db  \n",
       "15                   NaN          NaN  3bc52616bc739269  \n",
       "16                   NaN          NaN  d7f4a41055c2bcf7  \n",
       "17                   NaN          NaN  08383b3d43a8afe1  \n",
       "18                   NaN          NaN  359ce40cd4ef8942  \n",
       "19                   NaN          NaN  bb62d2a3b9830281  \n",
       "20                   NaN          NaN  34b9b02ef183447b  \n",
       "21                   NaN          NaN  58d065bcd14939f4  \n",
       "22                   NaN          NaN  c1dab65279348cfa  \n",
       "23                   NaN          NaN  5a6e0d7c39c7ec11  \n",
       "24                   NaN          NaN  5a6d7444d68667bb  \n",
       "25                   NaN          NaN  dd674104665c980a  \n",
       "26                   NaN          NaN  af57a162b13515c5  \n",
       "27                   NaN          NaN  5567042e7e4db835  \n",
       "28                   NaN          NaN  78a0fecec7c514d8  \n",
       "29                   NaN          NaN  ac809f5acd04178a  \n",
       "...                  ...          ...               ...  \n",
       "128352               NaN          NaN  43959476721f93fa  \n",
       "128353               NaN          NaN  aee185fb0302cffd  \n",
       "128354               NaN          NaN  f006a95f8ad98757  \n",
       "128355               NaN          NaN  8487712ac8ff5a84  \n",
       "128356               NaN          NaN  90e7cd112d95a7d8  \n",
       "128357               NaN          NaN  b4301454880f3846  \n",
       "128358               NaN          NaN  42bb9234f13072cc  \n",
       "128359               NaN          NaN  6d5f03a810aaffc0  \n",
       "128360               NaN          NaN  2b6dc59fe5b2bbf6  \n",
       "128361               NaN          NaN  29fa508549a53643  \n",
       "128362               NaN          NaN  429bc5343b90b2b2  \n",
       "128363               NaN          NaN  a0acc4c95f4d0ee1  \n",
       "128364               NaN          NaN  877f49679e777839  \n",
       "128365               NaN          NaN  4d395773f9d23d3d  \n",
       "128366               NaN          NaN  c1450c3690afede6  \n",
       "128367               NaN          NaN  4726ce14c84e25c4  \n",
       "128368               NaN          NaN  fa4be9943069b560  \n",
       "128369               NaN          NaN  7ad0d6cbf47cbf32  \n",
       "128370  5b1ab8c53fb1c23a          1.0  f1f3a30fb8acbb12  \n",
       "128371               NaN          NaN  ee325cc639aef5bc  \n",
       "128372               NaN          NaN  003130ba620b67c8  \n",
       "128373               NaN          NaN  36cdf55036b09e1c  \n",
       "128374               NaN          NaN  11112cd8ffedf099  \n",
       "128375               NaN          NaN  4d38e564aa1a0a01  \n",
       "128376               NaN          NaN  85268bc70a64059b  \n",
       "128377               NaN          NaN  8b1dea1d78898e08  \n",
       "128378               NaN          NaN  d4a48ef47816a56e  \n",
       "128379               NaN          NaN  b09c55c649d39617  \n",
       "128380               NaN          NaN  2c4d1c9ebfedc565  \n",
       "128381               NaN          NaN  b7aba2e726d0b518  \n",
       "\n",
       "[128382 rows x 27 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "test_trans\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "amt_0 = df2.sort_values('trans_amt')\n",
    "amt_1 = amt_0[amt_0.tag_sum_cnt!=0]\n",
    "amt_0 = amt_0[amt_0.tag_sum_cnt==0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "D:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "D:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n",
      "D:\\anaconda\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.JointGrid at 0x22781b00>"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.jointplot(data=amt_0, x='trans_amt', y='tag_sum_cnt')\n",
    "sn.jointplot(data=amt_1, x='trans_amt', y='tag_sum_cnt')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x22930cc0>"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "test_trans.trans_amt.hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
